Real-time Monitoring Stations Research Articles

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Analysis of landslide kinematics integrating weather and geotechnical monitoring data at Tan Son slow moving landslide in Ha Giang province

Rainfall infiltration on the slope increases pore-water pressure in the soil/ rock mass and may cause landslides. Therefore, the precipitation, pore-water pressure and displacement show the kinematic variation of a landslide. In recent years, several approaches have been proposed to investigate the kinematic behavior. Among them, the combination of time series data and geotechnical model has demonstrated its effectiveness in stage classification of landslide kinematics. This paper aims to analyze and classify the stages of landslide kinematic variation with integrating weather and geotechnical monitoring data of Tan Son market landslides. The real-time monitoring station for Tan Son slow-moving landslides were equipped with fixed-in-place inclinometer probes and standard pore-water pressure sensors. The results show that landslide kinematics consist of three stages: stabilization, accumulation, and displacement. The displacement of the landslides was heterogeneous process. The velocity of the landslides significantly increased when the pore-water pressure ratio (ru) at the sliding surfaces > 0.53. While it is almost impossible to notice any displacement when the ratio r­u < 0.45. During the displacement state, the trend of inverse velocity variation gradually decreases to near zero hour/mm. In addition, early warnings of landslide can be released based on the kinematic stages and changes in inverse velocity in this study.

Study Trend of PM10 as Air Pollutant in Chandigarh: Case Study

Abstract: In the current study, air quality analyses for particulate matter (PM10) were performed in the city of Chandigarh. To create average data for the city, data was gathered from a real-time monitoring station located in Chandigarh's sector 25. Every 24 hours, every month, and every season had average concentrations computed. The highest and lowest monthly PM10 concentrations were recorded in the months of October in 2020 and February in 2021, respectively, and August and September in 2020 and 2021, respectively. The monsoon season had the lowest quantities, whereas the winter season had the highest concentrations. Then, using the daily average data, statistical relationships between the air pollution and some meteorological characteristics were computed. The speed of the wind, the relative humidity, and sun radiations were taken into account.

Association of ambient ozone exposure with anxiety and depression among middle-aged and older adults in China: exploring modification by high temperature

Anxiety and depression are severe public health problems worldwide. The effects of ozone exposure on anxious and depressive symptoms remain largely unknown, especially in China. We evaluated the associations between ozone exposure and depression and anxiety among middle-aged and older adults across China. A multi-center community-based repeated measurement study among middle-aged and older adults was conducted from 2017 to 2018 in 11 provinces in China. The status of depression and anxiety was measured using Patient Health Questionnaire-9 (PHQ-9) and the generalized anxiety disorder seven-item (GAD-7) scale at the cut-off point of five, respectively. Concentrations of multiple ozone metrics were collected from real-time monitoring stations. The multilevel logistic regression model with random intercept was used to evaluate the effects of ambient ozone on anxiety and depression over different exposure windows. After adjusting for potential confounders, a 10 μg /m3 increase in the three months moving average of ozone was associated with the risk of anxiety [odds ratio (OR) = 1.25; 95% confidence interval (CI): 1.15; 1.37] and depression (OR = 1.17; 95% CI: 1.08; 1.27). A significantly positive modification effect of temperature on associations between ozone and anxiety was also found, while there is no interaction for depression. Exposure-response curves showed that there may be a threshold for the effect of ozone exposure on anxiety and depression over the three months moving average concentrations, with similar patterns observed at different temperature levels. People over 65 years old were at significantly higher risks of ozone-associated depression, while anxiety was more strongly associated with ozone in hypertensive patients. Our study supports the theory that anxiety and depression is associated with mid-term ozone exposure in China, and temperatures significantly enhanced their associations. These findings may have significant implications for promoting prevention activities regarding mental disorders and approaches in reducing the disease burden by simultaneously controlling air pollution and mitigating climate change.

Real-time acoustic observations in the Canadian Arctic Archipelago.

The main sources of noise in the Arctic Ocean are naturally occurring, rather than related to human activities. Sustained acoustic monitoring at high latitudes provides quantitative measures of changes in the sound field attributable to evolving human activity or shifting environmental conditions. A 12-month ambient sound time series (September 2018 to August 2019) recorded and transmitted from a real-time monitoring station near Gascoyne Inlet, Nunavut is presented. During this time, sound levels in the band 16-6400 Hz ranged between 10 and 135 dB re 1 μPa2/Hz. The average monthly sound levels follow seasonal ice variations with a dependence on the timing of ice melt and freeze-up and with higher frequencies varying more strongly than the lower frequencies. Ambient sound levels are higher in the summer during open water and quietest in the winter during periods of pack ice and shore fast ice. An autocorrelation of monthly noise levels over the ice freeze-up and complete cover periods reveal a ∼24 h periodic trend in noise power at high frequencies (>1000 Hz) caused by tidally driven surface currents in combination with increased ice block collisions or increased stress in the shore fast sea ice.

Smart and Sustainable Bioeconomy Platform: A New Approach towards Sustainability

The smart and sustainable bioeconomy represents a comprehensive perspective, in which economic, social, environmental, and technological dimensions are considered simultaneously in the planning, monitoring, evaluating, and redefining of processes and operations. In this context of profound transformation driven by rapid urbanization and digitalization, participatory and interactive strategies and practices have become fundamental to support policymakers, entrepreneurs, and citizens in the transition towards a smart and sustainable bioeconomy. This approach is applied by numerous countries around the world in order to redefine their strategy of sustainable and technology-assisted development. Specifically, real-time monitoring stations, sensors, Internet of Things (IoT), smart grids, GPS tracking systems, and Blockchain aim to develop and strengthen the quality and efficiency of the circularity of economic, social, and environmental resources. In this sense, this study proposes a systematic review of the literature of smart and sustainable bioeconomy strategies and practices implemented worldwide in order to develop a platform capable of integrating holistically the following phases: (1) planning and stakeholder management; (2) identification of social, economic, environmental, and technological dimensions; and (3) goals. The results of this analysis emphasise an innovative and under-treated perspective, further stimulating knowledge in the theoretical and managerial debate on the smart and sustainable aspects of the bioeconomy, which mainly concern the following: (a) the proactive involvement of stakeholders in planning; (b) the improvement of efficiency and quality of economic, social, environmental, and technological flows; and (c) the reinforcement of the integration between smartness and sustainability.

Digitalisation driven urban metabolism circularity: A review and analysis of circular city initiatives

Digitalisation of urban metabolism circularity provides policymakers, urban managers, planners and administrators with a useful tool for identifying, controlling and evaluating a wide range of data concerning the flows of social, environmental and economic resources. This approach is based on the crucial role of fixed and mobile digital infrastructures such as real-time monitoring stations, GPS tracking sensors, augmented reality, virtual sharing platforms, social media dashboards, smart grids, and the like in the development and strengthening of the quality and efficiency of the circularity of resources. For these reasons, the integration of digital technologies in mobility, waste, water and wastewater management, energy efficiency, safety, and so on, represents a crucial aspect for cities involved in the circularity of their urban metabolism. Through a systematic literature review and case study approaches, the analysis disclose a wide-range of initiatives adopted by several European circular cities that optimise the circularity of urban metabolic flows, and contributes to the efforts in increasing understanding and awareness of the digitalisation driven by the urban metabolism circularity.

Digital Technologies for Urban Metabolism Efficiency: Lessons from Urban Agenda Partnership on Circular Economy

Digital technologies engaged in urban metabolism for efficiency provide policymakers, urban managers, and planners with useful instruments to collect, monitor, analyze, and evaluate the circularity of environmental, social, and economic resources to improve their effectiveness and quality. At present, the digital technology-based approach is strategic for circular cities engaged in the development of smart and sustainable actions in the fields of mobility, energy, environment, waste, telecommunications, and security. Through the ‘Circular Resource Efficiency Management Framework’ developed by the European Commission, this paper generates insights into the digitalization practices of the circularity of urban metabolism by analyzing the initiatives implemented by the municipalities of Kaunas, Flanders region, Porto, Prato, The Hague, and Oslo, which constitute the Partnership on Circular Economy (PCE) of the Urban Agenda of the European Union. The results of the analysis provide a wide range of practices such as real-time monitoring stations for water and energy consumption, digital cameras for controlling vehicle flows, web platforms for sharing goods and services, and tracking sensors for public transport, which aim to optimize the efficiency of the circularity of urban metabolic flows. This study increases the understanding and awareness of digital technologies in this paradigm shift.

Smart and sustainable logistics of Port cities: A framework for comprehending enabling factors, domains and goals

Digital technologies integrated into port logistics are becoming increasingly decisive among port cities around the world. This growing importance is due to the need for policymakers, urban managers, port authorities, local administrators, shipping companies, couriers, and so on to develop increasingly digitalized and sustainable logistic processes. Therefore, in a global context characterized by intense datafication and globalization of trade, the data-based approach has become a necessary modus operandi to promote smart and sustainable logistics development. This forward-looking model of port logistics uses technologies such as IoT, sensors, cloud computing platforms, Big Data analytics, Artificial Intelligence (AI), GPS tracking systems, radars, drones, real-time monitoring stations, smart grids, and so on in order to collect, process, monitor and analyse data and information concerning the economic, environmental, social and technological sphere of port cities. In this sense, mobile and fixed platforms help logistics operators to optimize the management of flows (e.g., water, waste, emissions, raw materials, people, monetary investments, etc.) in an efficient and digitized manner. The study proposes a systematic literature review of the most recurring themes concerning smart and sustainable logistics initiatives within port cities in order to develop a multidimensional framework capable of holistically integrating the prevailing enabling factors (Ecosystem, Internal Organization, Data and Security, Policy and Regulation, Finance and Funding, and Digital and Technology), domains (Mobility, Environment, Economy, Telecommunications, Safety and Security, Government, and Community) and goals (Sustainable Development and Digitalization) that characterize smart and sustainable logistical development. To this end, the best practices of several pioneering port cities such as Rotterdam, Hamburg, Singapore, Los Angeles, Amsterdam, etc. implemented in partnerships with technology companies such as Cisco, IBM, Huawei and SAP were also analysed. Therefore, the results of this research show that smart and sustainable logistics initiatives in port cities: (a) have the potential to enhance the efficiency of the economic, environmental, social and technological flows; (b) increase the involvement and awareness of stakeholders such as couriers, shippers, shipping companies, citizens, port authorities, municipalities, security officers, gate and terminal personnel, and so on; and (c) provide a detailed overview of the enabling factors, domains and goals that must be activated by port cities to foster a smart and sustainable logistic transition.

Impact of exposure to ambient air pollutants on the admission rate of hospitals for asthma disease in Shiraz, southern Iran

Asthma is a common chronic respiratory disease in the world. Short-term exposure to ambient air pollutants is closely related to acute respiratory diseases and asthmatic symptoms. The purpose of this research was to estimate the correlation between exposure to three air pollutants (O3, NO2, and SO2) and hospital admission because of asthmatic disease (HAAD) in the city of Shiraz, southern Iran. The data were collected from the two real-time monitoring stations located in this city. The acquired information was used for developing predictive models by the AirQ software. The findings of this study were reported for two age groups (<15 and 15–64 years old). The highest levels of O3, NO2, and SO2 were obtained 187.33 μg/m3, 34.1 μg/m3, and 491.2 μg/m3 in 2016, respectively, and 227.75 μg/m3, 92.26 μg/m3, and 190.21 μg/m3, respectively, in 2017. Among the mentioned pollutants, the yearly average concentration of SO2 was 8.62 times more than the WHO guideline, during the studied times. The number of extra cases of HAAD for <15 years and 15–64 years caused by the air pollutants in Shiraz were estimated to be 273 and 36, respectively, in 2016, and 243 and 30 for 2017, respectively. The results of this work displayed that air pollutants have caused respiratory problems in Shiraz city. The AirQ model is a facile and potential tool for the prediction of asthma disease to reduce the health risk of atmospheric pollutants in the worldwide.

Understanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices.

The data-driven approach to sustainable urban development is becoming increasingly popular among the cities across the world. This is due to cities’ attention in supporting smart and sustainable urbanism practices. In an era of digitalization of urban services and processes, which is upon us, platform urbanism is becoming a fundamental tool to support smart urban governance, and helping in the formation of a new version of cities—i.e., City 4.0. This new version utilizes urban dashboards and platforms in its operations and management tasks of its complex urban metabolism. These intelligent systems help in maintaining the robustness of our cities, integrating various sensors (e.g., internet-of-things) and big data analysis technologies (e.g., artificial intelligence) with the aim of optimizing urban infrastructures and services (e.g., water, waste, energy), and turning the urban system into a smart one. The study generates insights from the sensor city best practices by placing some of renowned projects, implemented by Huawei, Cisco, Google, Ericsson, Microsoft, and Alibaba, under the microscope. The investigation findings reveal that the sensor city approach: (a) Has the potential to increase the smartness and sustainability level of cities; (b) Manages to engage citizens and companies in the process of planning, monitoring and analyzing urban processes; (c) Raises awareness on the local environmental, social and economic issues, and; (d) Provides a novel city blueprint for urban administrators, managers and planners. Nonetheless, the use of advanced technologies—e.g., real-time monitoring stations, cloud computing, surveillance cameras—poses a multitude of challenges related to: (a) Quality of the data used; (b) Level of protection of traditional and cybernetic urban security; (c) Necessary integration between the various urban infrastructure, and; (d) Ability to transform feedback from stakeholders into innovative urban policies.

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.

Asthma disease as cause of admission to hospitals due to exposure to ambient oxidants in Mashhad, Iran.

Nowadays, asthma is one of the most common chronic respiratory diseases, worldwide. Many reports have emphasized the correlation between the short-term exposure to the ambient air pollutants and acute respiratory diseases, especially among children with asthmatic symptoms. The aim of this study was to evaluate the relationship between the exposure to three atmospheric antioxidants (NO2, SO2, and O3) and hospital admission due to asthmatic disease (HAAD) in the city of Mashhad, Iran. The concentrations of atmospheric antioxidants were obtained from the real-time monitoring stations located in the city. The collected data were employed for developing predictive models in the AirQ software. In order to investigate the association between short-term exposure to air pollutants and HAAD, the study participants were categorized into two age groups: less than 15 and from 15 to 64years old. The results indicated that in people less than 15years increase in NO2 (attributable proportion (AP)=3.775%, 95% CI 0.897-6.883%), SO2 (AP=3.649%, 95% CI 1.295-5.937%), and O3 (AP=0.554%,95% CI 0.00-3.321) results in increase in HAAD. While for those aged between 15 and 64years, the AP was 4.192% (95% CI 0.450-7.662%) for NO2; 0.0% (95% CI 0.00-1.687%) for SO2; and 0.236% (95% CI 0.00-1.216%) for O3. The number of asthmatic cases who were less than 15years admitted to the hospitals during the study period was higher than that of those within the age groups between 15 and 64years as a consequence of exposure to NO2 (101 vs. 75), SO2 (98 vs. 0), and O3 (15 vs. 3), respectively. To the best of our knowledge, the AirQ model has not been applied before to estimate the effect of atmospheric antioxidant exposure on hospital admission because of asthma disease. Eventually, this model is proposed to be applicable for other cities around the world.

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (−370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.

A measure system of zero moment point using wearable inertial sensors

Keeping balance is the premise of human walking. ZMP(zero moment point) is a point where total torque achieves balance. It is an important evaluation parameter of balance ability in walking, since it can be used to better reflect the dynamic balance during walking. ZMP can be used in many applications, such as medical rehabilitation, disease diagnosis, treatment and etc. In this paper, wearable inertial sensors system based on MEMS is used to measure ZMP (zero moment point) during walking, which is cheap, convenient, and free from the restriction of lab. Our wearable ZMP measurement system consists of inertial measurement subsystem and PC real-time monitoring station. Inertial measurement subsystem includes 9-axis inertial sensing nodes, the body communication network and the central node. Inertial sensing nodes are mounted on different parts of the body to collect body posture information in real-time, and then the best estimation of current posture are obtained by Kalman filter. The data from sensors is aggregated to the central node through the CAN bus, and then ZMP is calculated. Finally, it can be showed in the PC monitoring station. Experiments prove the system can achieve real-time ZMP detection during walking.

Thailand radiation monitoring program: a preliminary result analysis of the country's background radiation

Environmental monitoring in the context of nuclear-related activities refers to the measurements of ambient gamma dose rates in the environment and radioactivity in air, water, soil etc. Since nuclear power programme in Thailand has not yet been in place, the environment surveillance programme in Thailand currently focuses on establishing 'baseline' environmental radiation levels and radioactivity for the purpose of establishing impacts of future possible sources such as nuclear accidents. The objective is to assure assessments concerning radiation safety for the environment and public. At present, Thailand's monitoring programme operates eight fixed monitoring stations installed in various regions of Thailand; four of which have been in operation since late 2010. The ambient gamma dose rate data are routinely collected from those four real-time monitoring stations starting from October 2010 to September 2011 as described in this study. Also, the radiation data from each station are statically analysed. This study found that the dose rate from the stations located in north and north-east of Thailand showed an apparent tendency towards a diurnal pattern. With these routine data, the average exposure dose rate was possible to estimate in Thailand. Additionally, the assessment of the equivalent dose from natural background radiation was estimated.

A design proposal of real-time monitoring stations: implementation and performance in contrasting environmental conditions

With the aim of creating a real-time monitoring network for both oceanographic and meteorological data, a monitoring station conceptual design was developed. A common framework for software and electronics was adapted to different environmental conditions using two buoy approaches: one intended for oceanic waters, to be moored up to 30-40 m depth, where waves are the critical design factor, and one for continental waters (rivers, lakes and the inner part of estuaries), where currents are the critical design factor. When structures such as bridges are present in the area, the monitoring station can be installed on these structures, thus reducing its impact and increasing safety. In this paper, the design, implementation, operation and performance of these stations are described. A reliability index is calculated for the longest time series of the three related deployment options on the Galician coast: Cies (oceanic buoy in front of the Ria de Vigo), Catoira (continental buoy in the Ulla river) and Cortegada (installation in a bed in the Ria de Arousa).