5G NR launching in Greece: Preliminary in situ and monitoring network measurements of electromagnetic fields exposure levels at rooftops.

In Greece, 5G New Radio (NR) has started launching in the end of 2020, at the 3400-3800 MHz (FR1) frequency band. Focusing on 117 Base Stations (BSs) which were already equipped with 5G NR antennas, in situ broadband and frequency selective measurements have been conducted at minimum three points of interest, at adjacent rooftops (when accessible). The points have been selected according to the sweeping method and the electric field strength (E) value has been stored on the selected worst-case scenario point. Spectrum analysis was conducted in the FR1, for the allocated spectrum that corresponds to each mobile communication provider, in order to get preliminary results concerning the contribution of the 5G NR emissions in the general public exposure levels. The vast majority of the in situ measurements has been conducted in urban environments from the beginning of 2021 until the mid of 2022, since in Greece 5G NR services launching started from the big cities. Additionally, a 5G NR BS, installed in a suburban environment (in the city of Kalamata) is thoroughly investigated during its pilot and regular operation, based on broadband and frequency selective measurements data derived by the National Observatory of Electromagnetic Fields (NOEF) monitoring sensor network. In situ measurement data within the 5G NR frequency range are verified via the NOEF's output. The 5G NR contribution to the total E-field levels is assessed in time, from pilot to regular operation of the BS. In all cases, compliance with the reference levels for general public exposure is affirmed.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China.

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Optimal design of groundwater pollution monitoring network based on a back-propagation neural network surrogate model and grey wolf optimizer algorithm under uncertainty.

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation-optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty.

Where to measure water quality ? Application to nitrogen pollution in a catchment in France.

Information on the water quality of rivers can be used to judge the effectiveness of past policies or to guide future environmental policies. Consequently, the location of water quality monitoring stations (WQMSs) plays an important role in river pollution control. In the 2000s, a literature developed on the optimization of WQMS location to identify pollution hot spots, average quality, or to minimize the detection time of a potential source of accidental pollution. This article is part of a new literature aimed at locating WQMSs in order to optimize the economic value of information (EVOI) generated by water quality monitoring networks (WQMNs). The field of study is a catchment in northeastern France where the purpose of quality measurement is to define a policy of reduction of agricultural nitrogen fertilizers in order to reach the standard of 50 mg/l of nitrate at the WQMS. Agro-hydrological and economic models estimate the net benefit of input reduction depending on the location of the WQMS on the basis of different assumptions concerning the ecological damage generated by nitrate. We show that the magnitude of the ecological damage and, consequently, the perception of the contamination generated by nitrate in water, play a decisive role on the optimal location of the WQMS, as well as on the benefit of the economic optimization of locations, compared to traditional optimization. Locating WQMSs in a way that maximizes EVOI will be more attractive for very high or very low levels of damage. However, in this context, linking damage to nitrate concentration or to concentration coupled with riparian population density alone will have little impact.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500 m * 500 m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986-2019.

Wet deposition monitoring is a critical part of the long-term monitoring of acid deposition, which aims to assess the ecological impact of anthropogenic emissions of SO2 and NOx. In North America, long-term wet deposition has been monitored through two national networks: the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program (NADP), for Canada and the USA, respectively. In order to assess the comparability of measurements from the two networks, collocated measurements have been made at two sites, one in each country, since 1986 (Sirois et al., in Environmental Monitoring and Assessment, 62, 273-303, 2000; Wetherbee et al., in Environmental Monitoring and Assessment, 1995-2004, 2010). In this study, we compared the measurements from NADP and CAPMoN instrumentation at the collocated sites at the Pennsylvania State University (Penn State), USA, from 1989 to 2016, and Frelighsburg, Quebec, Canada, from 2002 to 2019. We also included in the study the collocated daily-vs-weekly measurements by the CAPMoN network during 1999-2001 and 2016-2017 in order to evaluate the differences in wet concentration of ions due to sampling frequency alone. The study serves as an extension to two previous CAPMoN-NADP inter-comparisons by Sirois et al. (Environmental Monitoring and Assessment, 62, 273-303, 2000) and Wetherbee et al., in (Environmental Monitoring and Assessment, 1995-2004, 2010). At the Penn State University site, for 1986-2019, CAPMoN was higher than NADP for all ions, in terms of weekly concentration, precipitation-weighted annual mean concentration, and annual wet deposition. The precipitation-weighted annual mean concentrations were higher for SO42- (2%), NO3- (12%), NH4+ (16%), H+ (6%), and base cations and Cl- (11-15%). For annual wet deposition, CAPMoN was higher for SO4-2, NO3-, NH4+ and H+ (5-17%), and base cations and Cl- (12-17%) during 1986-2019. At the Frelighsburg site, NADP changed the sample collector in October 2011. For 2002-2011, the relative differences at the Frelighsburg site were positive and similar in magnitude to those at the Penn State site. For 2012-2019, the precipitation-weighted annual mean concentrations were 5-27% lower than NADP, except for H+, which was 23% higher. The change in sample collector by NADP had the largest effect on between-network biases. The comparisons of daily-vs-weekly measurements conducted by the CAPMoN network during 1999-2001 and 2016-2017 show that the weekly measurements were higher than the daily measurements by 1-3% for SO42-, NO3-, and NH4+; 3-9% for Ca2+, Mg2+, Na+, and Cl-; 10-24% for K+; and lower for H+ by 8-30% in terms of precipitation-weighted mean concentration. Thus, differences in sampling frequencies did not contribute to the systematically higher CAPMoN measurements. Understanding the biases in the data for these networks is important for interpretation of continental scale deposition models and transboundary comparison of wet deposition trends.

Machine learning-based optimal design of groundwater pollution monitoring network.

It is an important task of environmental management to design groundwater pollution monitoring network (GPMN) to find out the occurrence of pollution events and carry out remediation in time. However, there are many uncertain factors in the process of designing GPMN, which affect the GPMN design result. In the process of applying the Monte Carlo method for uncertainty analysis, groundwater numerical simulation model may be utilized thousands of times, which results in a huge computational load. In order to overcome this disadvantage, a machine learning (ML)-based surrogate model is constructed with Kriging method, to replace the computational simulation model under uncertainty of pollution sources and parameters. The 0-1 integer programming optimization model is constructed to maximally cover serious polluted area to detect the occurrence of groundwater pollution in time. The optimal design framework of GPMN based on proposed ML algorithm was applied in a domestic landfill in Baicheng City, China. The results showed that the ML-based surrogate model has a great fitness with the groundwater solute transport simulation model. The optimal results of GPMN indicated that monitoring wells should be mainly placed at the downstream of the leachate equalization basin. If more wells are allowed to be placed, part of wells could be placed at the downstream of the landfill. Moreover, the area where the pollution plumes of landfill site meet that of leachate equalization basin should be set as the key monitoring objective. Verification and comparison showed that the pollutant detection rate of the optimal layout scheme is far higher than random layout schemes, which proves the reliability of the ML-based optimal design scheme of GPMN.

Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT.

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called 'prosumers' and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms.

Useful tools for integrating noise maps about noises other than those of transport, infrastructures, and industrial plants in developing countries: Casework of the Aburra Valley, Colombia.

The behavior of environmental noise in developing countries is conditioned by characteristics that are not only linked to transport, infrastructures, and industrial plants in the annuity (common representation in noise maps), but also to other types of sources and periodicities that can influence significantly in noise levels. For this reason, this work proposes different temporal analyzes during the annuity that can be linked to the noisy activities typical of developing tropical countries. To do this, a noise monitoring network composed of seven monitors representing different sources present in the Aburrá Valley (AV) in Colombia is analyzed with measurements of LAeq, every hour, in a period between August 2016 and July 2019. The results show that AV noise is strongly influenced by leisure activities related to high-power sound systems, different celebrations, and continuous noise from car traffic that affect the population mainly on weekends and nights. This work marks a clear path to precisely address noise pollution in the action plans of developing countries.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18.

China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM2.5 concentration slightly lower by 0.6-2.2 µg m-3 and 1.4-6.0 µg m-3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014-15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.

Impact of COVID-19 lockdown on ambient noise levels in seven metropolitan cities of India.

The paper analyzed the impact of lockdown on the ambient noise levels in the seventy sites in the seven major cities of India and ascertained the noise scenario in lockdown period, and on the Janta Curfew day in comparison to the pre-lock down period and year 2019 annual average values. It was observed that the majority of the noise monitoring sites exhibited a decrement in ambient day and night equivalent noise levels on the national Janta Curfew day and Lockdown period as compared with the normal working days attributed to the restricted social, economical, industrial, urbanization activity and reduced human mobility. A mixed pattern was observed at a few sites, wherein the ambient day and night equivalent noise levels during Janta curfew day and Lockdown period had been reported to be higher than that on the normal working days. The study depicts the noise scenario during the lockdown and pre-lockdown period for seventy sites in India and shall be instrumental in analyzing the consequences and implications of imposing lockdowns in future on the environmental noise pollution in Indian cities.

Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction.

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term "endocyclic" refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

Night sky brightness monitoring network in Wuxi, China.

The rapid development of cities has brought tremendous pressure to astronomical observation, energy security, and the ecosystem. Automatic monitoring of night sky brightness (NSB) can help us to understand its regional differences and time variations of NSB effectively and to investigate the human and natural factors which lead to these changes. In this paper, the construction of Wuxi City night sky brightness monitoring network (WBMN) in China is presented. In addition to introducing the equipment and the installation of the network, a brief analysis of the data obtained from the stations will also be presented. The impact of human activities on the NSB is illustrated through its changes during the Spring Festival (lunar new year) and non-festival nights, and through a comparison study between NSB data taken from locations of different land usages. It is concluded that, while the reduction in human activities after non-festival midnights or the reduction in moon illumination near the new moon epoch led to darker night skies, brightening of the night skies may be attributed to firework displays during the nights of Spring Festival in 2019. On the other hand, the absence of firework during the Spring Festival in 2020 may explain the darker night skies. Finally, there is an evidence that the urban developments in Wuxi are degrading night sky quality.

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil.

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources.

Landscape-scale approaches for enhancing biological pest control in agricultural systems.

Over the last decades, land management options have been investigated that aim at enhancing services to agriculture delivered by biodiversity and its associated biotic interactions. Such services can be promoted through land management strategies ranging from in-field single agricultural practices, long-term strategies compiling these agricultural practices at the crop rotation scale, to management strategies at the landscape scale. In this paper, we provide an overview of the land management options that can be implemented at multiple scales, with a specific focus on the provision of one service that is key in agriculture, i.e. pest control. We present existing knowledge and highlight current gaps and limitations in our understanding of pest control response to land management. Based on this analysis, we propose two promising and complementary research approaches that could help filling existing knowledge gaps and provide guidelines for designing landscapes for agroecological services: (1) landscape monitoring networks (LMN), based on long-term monitoring of ecological and managerial processes within sets of landscapes located in contrasted production contexts; (2) agroecological system experiments (ASE), which design and assess combinations of land management options at multiple embedded spatial scales.

Spatiotemporal trends of PM2.5 and its major chemical components at urban sites in Canada.

To evaluate the effectiveness of emission control regulations designed for reducing air pollution, chemically resolved PM2.5 data have been collected across Canada through the National Air Pollution Surveillance network in the past decade. 24-hr time integrated PM2.5 collected at seven urban and two rural sites during 2010-2016 were analyzed to characterize geographical and seasonal patterns and associated potential causes. Site-specific seven-year mean gravimetric PM2.5 mass concentrations ranged from 5.7 to 9.6 µg/m3. Seven-year mean concentrations of SO42-, NO3-, NH4+, organic carbon (OC), and elemental carbon (EC) were in the range of 0.68 to 1.6, 0.21 to 1.5, 0.27 to 0.71, 1.1 to 1.9, and 0.37 to 0.71 µg /m3, accounting for 10.8%-18.1%, 3.7%-16.7%, 4.7%-7.4%, 18.4%-21.0%, and 6.4%-10.6%, respectively, of gravimetric PM2.5 mass. PM2.5 and its five major chemical components showed higher concentrations in southeastern Canada and lower values in Atlantic Canada, with the seven-year mean ratios between the two regions being on the order of 1.7 for PM2.5 and 1.8-7.1 for its chemical components. When comparing the concentrations between urban and rural sites within the same region, those of SO42- and NH4+ were comparable, while those of NO3-, OC, and EC were around 20%, 40%-50%, and 70%-80%, respectively, higher at urban than rural sites, indicating the regional scale impacts of SO42- and NH4+ and effects of local sources on OC and EC. Monthly variations generally showed summertime peaks for SO42- and wintertime peaks for NO3-, but those of NH4+, OC, and EC exhibited different seasonality at different locations.

A methodological framework for improving air quality monitoring network layout. Applications to environment management.

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network (AQMN). It requires to be constituted by a minimum and reliable number of measurement sites. Nevertheless, the AQMN efficiency should be assessed over time, as a consequence of the possible emergence of new emission sources of air pollutants, which could lead to variations on their spatial distribution within the target area. PM10 particles data monitored by the Community of Madrid's (Spain) AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance. The annual spatial distribution of average PM10 levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system (GIS), and the percentage of similarity between both postulates was quantified using simple linear regression (> 95%). As one innovative tool of this study, the practical application of the proposed methodology was validated using PM10 particles data measured by AQMN during 2007 and 2018, reaching a similitude degree higher than 95%. The influence of temporal variation on the proposed methodological framework was around 20%. The proposed methodology sets criteria for identifying non-redundant stations within AQMN, it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations, which could help to tackle efforts to improve the air quality management.

Meta-analytic evidence for a joint neural mechanism underlying response inhibition and state anger.

Although anger may weaken response inhibition (RI) by allowing outbursts to bypass deliberate processing, it is equally likely that RI deficits precipitate a state of anger (SA). In adolescents, for instance, anger occurs more frequently and often leads to escalating aggressive behaviors. Even though RI is considered a key component in explaining individual differences in SA expression, the neural overlap between SA and RI remains elusive. Here, we aimed to meta-analytically revisit and update the neural correlates of motor RI, to determine a consistent neural architecture of SA, and to identify their joint neural network. Considering that inhibitory abilities follow a protracted maturation until early adulthood, we additionally computed RI meta-analyses in youths and adults. Using activation likelihood estimation, we calculated twelve meta-analyses across 157 RI and 39 SA experiments on healthy individuals. Consistent with previous findings, RI was associated with a broad frontoparietal network including the anterior insula/inferior frontal gyrus (aI/IFG), premotor and midcingulate cortices, extending into right temporoparietal areas. Youths showed convergent activity in right midcingulate and medial prefrontal areas, left aI/IFG, and the temporal poles. SA, on the other hand, reliably recruited the right aI/IFG and anterior cingulate cortex. Conjunction analyses between RI and SA yielded a single convergence cluster in the right aI/IFG. While frontoparietal networks and bilateral aI are ubiquitously recruited during RI, the right aI/IFG cluster likely represents a node in a dynamically-adjusting monitoring network that integrates salient information thereby facilitating the execution of goal-directed behaviors under highly unpredictable scenarios.

Optimal Design of Water Quality Monitoring Networks in Semi-Enclosed Estuaries.

The semi-enclosed estuary is very susceptible to changes in the physical and environmental characteristics of the inflow from the land. Therefore, continuous and comprehensive monitoring of such changes is necessary for managing the estuary. Nevertheless, the procedure or framework has not been proposed appropriately to determine how many instruments are necessary and where they need to be monitored and standardized to detect critical changes. The present work proposes a systematical strategy for the deployments of the monitoring array by using the combination of graphical optimization with the objective mapping technique. In order to reflect the spatiotemporal characteristics of the bay, the representative variables and eigenvectors were determined by the Empirical Orthogonal Function (EOF), and the cosine angle among them calculated and used as a design index of optimization. At the recommended locations, the sampled representative variables were interpolated to reconstruct their spatiotemporal distribution and compared with the true distribution. The analysis confirmed that the selected locations, even with a minimal number of points, can be used for on-site monitoring. In addition, the present framework suggests how to determine installable regions for real-time monitoring stations, which reflect the global and local characteristics of the semi-enclosed estuary.