Local Monitoring Data Research Articles

Interpretable feature selection and deep learning for short-term probabilistic PV power forecasting in buildings using local monitoring data

Accurate probabilistic forecasting of photovoltaic (PV) power is crucial for optimizing energy scheduling in smart buildings and ensuring the low-carbon, efficient operation of building energy management systems (BEMS). However, existing feature selection techniques fail to guarantee that the selected features genuinely impact the output of forecasting models. Additionally, traditional black-box deep learning models lack clarity on whether their output truly relies on those selected features. These challenges limit the accuracy of forecasting models. To address these challenges, a novel methodology named temporal importance model explanation and temporal fusion transformers (TIME-TFT) model is proposed. Firstly, the TIME method is employed for feature selection, and interpretable outputs are used to identify important global features. Secondly, the TFT model is then utilized for forecasting tasks, providing interpretable outputs to track back to the features that TFT model pays attention to. Finally, the consistency between the interpretable outputs of TIME method and TFT model is examined to confirm predictions are based on genuinely learned selected features. Empirical studies demonstrate the superiority of the proposed TIME-TFT system, outperforming comparable models with an R2 of 0.9546. In summary, the interpretable outputs not only improve the accuracy of predictions but also provides visual evidence for predictions, thereby bolstering effectiveness and credibility in engineering practices.

Establishing an Early Warning System for Dust Storms in Peri-Desert Regions

The Taklimakan Desert in northwest China stands as a significant contributor to dust storms, with its fringe oases already designated as ecologically fragile due to the severe impacts of these storms. This study focuses on Moyu County, situated on the southwest edge of the Taklimakan Desert, examining the origin and transport pathways of dust storms from 2004 to 2021. The classification involves utilizing a 36 h backward trajectory model and the k-means clustering technique, resulting in three clusters displaying distinct transport pathways and entry directions. Air pollutant concentrations at the study site corresponding to each cluster are analyzed to elucidate the contribution of dust storms from different directions. The results categorize 1952 dusty days into three categories: NE-SE (cluster 1), N-N (cluster 2), and NW-W (cluster 3). The highest frequency of dust storms, accounting for 64% of the total suspended dust weather, originates from the northeast and southeast direction (NE-SE category), with relatively weak intensity, mainly as suspended dust (71.5%). Strong sand storms predominantly occur from the northwest direction (57.8%). Cluster 1 (the southeast direction) exhibits a higher concentration of SO2, NO2, and CO, mainly associated with its pathway over anthropogenically polluted areas. Conversely, Cluster 3 (northwest direction) shows higher PM10 and PM2.5 concentrations due to increased wind speed and stronger dust storm intensity. The study develops dust storm early warning schemes based on 15-day advance predictions, utilizing an 18-year trajectory model and local monitoring data. This proposed warning scheme serves as a predictive tool for potential dust storm events and air pollution levels, aiding in both scientific research and policy formulation for dust storm mitigation and adaptation. The data obtained also hols relevance for conducting further scientific research in this field.

Проблемные вопросы локального мониторинга земель территорий традиционного природопользования при оценке воздействия нефтегазовых месторождений (на примере Ханты-Мансийского автономного округа – Югры)

In this article, the object of the study is the scientific foundations and methodology of local land monitoring in traditional environmental management territories, as specially protected areas. Its priority purpose is for traditional environmental management Issues of local land reliability monitoring data and methods of obtaining them have remained relevant for more than 20 years. With the development of digital technologies and employing remote sensing, the monitoring procedure is faster and more efficient, however, the information retrieved in this way requires verification and this currently can only be done through conducting field surveys of the territory and using instrumental methods control. The obtained local monitoring data enables resolving environmental and economic issues related to the management of land resources in traditional environmental approach areas of the Extreme North at the local level. The purpose of the study is to improve the scientific and methodological foundations of local monitoring in above mentioned zones (hereinafter referred to as TMP), taking into account the regional characteristics of the pointed territories. The theoretical and methodological foundations for that in Khanty-Mansi Autonomous Okrug – Yugra are considered; the impact of oil and gas production facilities on the ecological state of land resources was determined; proposals for improving the control at the TTP for traditional environmental management are substantiated

High-resolution air quality forecasts for LMIC using a combination of machine learning and earth model simulations

Background and aim: With the recent advancement of Earth observation, computer simulations, and low-cost monitoring technologies, the capacity to obtain accurate geospatial data has increased tremendously, particularly for locations without expansive in-situ monitoring networks. Here we present an optimized machine learning model to estimate near real-time air pollutant concentrations in selected locations in Africa and Latin America using a combination of NASA Goddard Earth Observing System Composition Forecasts (GEOS-CF) and low-cost sensor data. Methods: We use a machine learning approach to estimate near real-time air pollutants concentration at selected locations across Africa and Latin America. Several meteorological and chemical parameters are retrieved from NASA’s GEOS-CF to train a bias corrector model and predict corrected concentration estimates for these locations which are then validated against local monitoring data. We also conduct an explainability approach via SHAP Analysis to quantify the model contributing factors across these locations and track the model performance in extreme conditions. Results: The optimized machine learning model shows good agreement with ground air quality data, with R2 values from 0.61- 0.65 for locations in Mexico City (Mexico), Bogotá (Columbia) and Kigali (Rwanda). Via SHAP analysis, we demonstrate the need to conduct a measure of variance to determine model performance for different conditions and intervals, knowing that the model performance can be affected by various training conditions. Conclusion: Combining observations and optimized model simulations using machine learning techniques can significantly improve air quality forecasts in low- and middle-income countries, where the rapid pace of industrialization and communities are highly susceptible to air pollution health effects, and rarely have local air quality data and health risks alerting systems in place. These results are being used to assist air quality managers and environmental agencies in these locations to improve risk communication and reduce health burdens associated with outdoor air pollution.

BUILDING A TOMOGRAPHIC VELOCITY MODEL FOR SAMOYLOV ISLAND AREA (LENA DELTA) FROM LOCAL SEISMOLOGICAL DATA FOR THE PERIOD OF 2019–2021

In our paper we present the results of seismotomographic inversion for the local seismological monitoring data obtained in the area of the Samoylov Island (Lena River delta) in 2019–2021. Tomographic velocity model was used for refining hypocenter locations of local earthquakes and for geological interpretation. The results are shown as maps of anomalies of seismic waves and Vp /Vs ratios for the 5 and 10 km depths. The velocity anomalies structure made it possible to interpret low Vp /Vs ratio as rocks related to the Siberian platform, and to compare between the boundary of the low Vp /Vs area and the trace of the known geological fault running along the Olenekskaya Channel.

Changes in plant species richness due to land use and nitrogen deposition across the globe

AbstractAimThe effects of land use and atmospheric nitrogen (N) deposition on plant species richness are typically studied in isolation. Here, we quantified the combined effects of these two pressures on terrestrial plant species richness at a 0.25º spatial resolution across the globe.LocationGlobal.MethodsWe first used local monitoring and experimental data to determine the proportional changes in plant species richness in relation to different land‐use types and N addition levels according to a meta‐analytical approach. We then developed a new multi‐pressure species–area relationship (mp‐SAR) model to combine the site‐level responses resulting from the meta‐analyses with global land use and N deposition maps to calculate changes in plant species richness at a resolution of 0.25º across the globe.ResultsBoth pressures combined resulted in an average plant species richness decline of 26% (± 12% SD) across the grid cells. The combined impact was the largest in Europe, with an average decline of 34% (± 8%), and the smallest in South America (16% ± 11%). Overall, species declines due to land use were considerably larger (19% ± 11%) than declines due to N deposition (6% ± 6%).Main conclusionsOur new species–area model allows for systematic large‐scale assessments of the combined effects of multiple pressures on species richness based on easily retrievable input data. Overall, land use is a more important driver of plant species richness decline than N deposition, but N deposition may have considerable additional impact in specific regions of Europe, Asia and North America. These findings imply that conservation efforts should simultaneously tackle both pressures to ensure their effectiveness in preserving plant biodiversity.

Significance of gram-resistant flora in etiology of nosocomial infections in determining rational antibiotic therapy

In recent years, the problem of resistance of gram-negative microorganisms to carbapenems, as well as the emergence of multiresistant strains is gaining urgency. This problem is of particular importance in neonatal practice due to the age limitations of the use of many antibiotics and the lack of specific recommendations. Local bacteriological monitoring data should be taken into account when selecting a starting antibiotic, and for this purpose, methods of rapid identification of the pathogen and its resistance mechanisms should be introduced. Pediatricians need controlled trials of new antibiotics to successfully treat MDR-Gram-infections.

Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration

Restoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30–60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.

GAMBARAN PENGETAHUAN KADER TENTANG DETEKSI RISIKO TINGGI IBU HAMIL DI WILAYAH KERJA PUSKESMAS BERANGAS

Local Area Monitoring Data for Maternal and Child Health (PWS-KIA) in Barito Kuala Regency, the coverage of detection of high-risk pregnant women by health workers is 47.3% of the 20% target, detection by cadres is 13.1% of the 80% target, and 2018 data regarding the coverage of detection of at-risk pregnant women by health workers at 47.2% of the 20% target and detection by cadres of 8.5% of the 80% target. This study was conducted with the aim of knowing the description of cadres' knowledge about detection of high risk of pregnant women in the work area of the Berangas Health Center. This study used a descriptive research method. The population and sample were all cadres who were in the work area of the Berangas Health Center. The data obtained using primary data and secondary data. The results of the study were 15 people (50%) had good knowledge, 12 people (40%) had sufficient knowledge and 3 people (10%) had poor knowledgeThe results of this study can be concluded that the level of knowledge of cadres about high risk detection of pregnant women in the work area of the Berangas Health Center is good.

Towards the progress of ecological restoration and economic development in China's Loess Plateau and strategy for more sustainable development

China's Loess Plateau (LP) is one of the most typical regions with serious soil erosion. Its ecological restoration, economic development, and social progress are directly related to realizing China's national sustainable development goals (SDGs). With intensive field investigations, comprehensive analyses of statistical data, remote sensing data, and local monitoring data, this current research explored the achievements in local ecological restoration and economic development, discussed the main problems faced by the LP, and assessed the sustainable development status of the LP. Moreover, drawing from a 3C system approach (classification–coordination–collaboration), this research proposed the sustainable development strategies for more sustainable LP. We found that the LP's ecological restoration and economic development made remarkable achievements since 2000. However, the environment was still fragile and regional development was still lagging behind. In realizing SDGs, further improvements on clean water and sanitation, decent work and economic growth, industry, innovation and infrastructure, reduced inequalities, and climate action are needed. Coordinated and collaborated efforts are expected from multiple hierarchical levels including household, watershed, region, and nation in developing LP. Specifically, human-environment interactions should be highlighted at the household level, upstream-downstream interactions that are catering to the particular characteristics of the LP is encouraged at the watershed level, and regional and urban-rural interactions shall play an increasingly important role at the regional level. While at the national level, the central government should carry out a systematic overarching design. To better realize the goal of more sustainable LP, this research calls for (1) forming an actor-network consists of multi stakeholders, including the central government, local government, watershed, families, ecosystem and environment, enterprises, NGOs, and scientists, and (2) paying attention to ecosystem management, ecological restoration, industrial structure optimization, ecological agriculture, rural infrastructure, and ecological civilization construction in the future development.

Статистика Getis-Ord Gi* при обработке многолетних данных содержания углеводородов в бассейне реки Большой Балык

Balyk river basin for the period 2006–2017 using ArcGIS Pro statistical analysis tools are presented in this article. The information basis of the research is the local environment monitoring data of license areas of Ugra, Khanty-Mansiysk Autonomous Okrug, RF. The research was implemented in two stages. At the first stage, pollution hot spots were revealed basing on the calculation of local Getis-Ord Gi* index for each year. The calculation was made taking into account the mutual location of sampling points and value of the neighborhood. At the second stage hot spots genesis for 12 years was analyzed via modelling space-and-time cube. Clustering time series of hydrocarbons average annual concentration according to the Getis-Ord Gi* indicator made it possible to determine the places of one-off pollution, most likely associated with oil spills, and to track pollutants transportation along the current. The location of the increasing river ecosystem pollution was also determined. The obtained results enable bringing out basic zones of permanent high hydrocarbon concentrations and places of periodic discharges into the river basin.

CMS data access and usage studies at PIC Tier-1 and CIEMAT Tier-2

The current computing models from LHC experiments indicate that much larger resource increases would be required by the HL-LHC era (2026+) than those that technology evolution at a constant budget could bring. Since worldwide budget for computing is not expected to increase, many research activities have emerged to improve the performance of the LHC processing software applications, as well as to propose more efficient resource deployment scenarios and data management techniques, which might reduce this expected increase of resources. The massively increasing amounts of data to be processed leads to enormous challenges for HEP storage systems, networks and the data distribution to end-users. These challenges are particularly important in scenarios in which the LHC data would be distributed from small numbers of centers holding the experiment’s data. Enabling data locality relative to computing tasks via local caches on sites seems a very promising approach to hide transfer latencies while reducing the deployed storage space and number of replicas overall. However, this highly depends on the workflow I/O characteristics and available network across sites. A crucial assessment of how the experiments are accessing and using the storage services deployed in WLCG sites, to evaluate and simulate the benefits for several of the new emerging proposals within WLCG/HSF. Studies on access and usage of storage, data access and popularity studies for the CMS workflows executed in the Spanish Tier-1 (PIC) and Tier-2 (CIEMAT) sites supporting CMS activities are reviewed in this report, based on local and experiment monitoring data, spanning more than one year. This is of relevance for simulation of data caches for end-user analysis data, as well as identifying potential areas for storage savings.

The macroscopic mechanisms and associated atmospheric precursor environmental capacities that lead to secondary fine particle pollution

This paper establishes the kinetic equations in atmospheric chemistry that describe the macroscopic mechanisms of secondary fine particle pollution generated by precursors during atmospheric self-purification. The dynamic and static solutions of these equations can be applied to calculate quantitative relationships between the concentration ratio of precursors and secondary fine particles as well as the physical clearance power of the atmosphere, chemical reaction rate, and the scale of a contaminated area. The dynamic solution presented here therefore corresponds with a theoretical formula for calculating the overall rate constant for the oxidation reaction of reducing pollutants in the actual atmosphere based on their local concentrations and meteorological monitoring data. In addition, the static solution presented in this paper reveals the functional relationship between the concentration of secondary fine particles and precursor emission rate as well as atmospheric self-purification capacity. This result can be applied to determine the atmospheric environmental capacity of a precursor. Hourly records collected over the last 40 years from 378 weather stations in mainland China as well as the spatiotemporal distribution sequence of overall oxidation reaction rates from precursors show that when the reference concentration limit of secondary fine particles is 100 μmol m-3, the atmospheric environmental capacity of total precursors can be calculated as 24890×1010 mol yr-1. Thus, when the annual average concentration limit of given fine particles is 35 μg m-3 and the ratio of sulfate and nitrate to 30% and 20% of the total amount of fine particles, the capacities of SO2, NOx and NH3 are 1255, 1344, and 832 (1010g yr-1), respectively. The clearance density of precursors for different return periods across mainland China under above conditions are also provided in this study.

Predicting coastal fishing community characteristics in Tanzania using local monitoring data.

Small-scale marine fisheries in Tanzania provide the main source of subsistence for coastal communities, yet due to poor management, they have been overexploited for decades. These coastal fisheries have historically been described as homogeneous in gear-use and fish community makeup. Yet, regional and local variability in the characteristics of these fishing communities was recently identified with community-based fisheries-dependent data. We proposed a flexible modeling approach that incorporated local monitoring data with spatial data to predict the spatial characteristics of the marine fisheries in Tanzania. The spatial models identified relationships between fishery landings and coral reef, seagrass, and mangrove habitat patch attributes, along with fisher density and a hydrologic index. Furthermore, the predicted spatial characteristics matched previously reported fishery characteristics in both districts. The maps developed by our modeling process provide a means for stakeholders and managers to understand the spatial distribution of their fisheries and in turn, focus on explicitly managing what, how, and where fishers operate. Overall, the flexible modeling approach developed here may act as a first step in incorporating local monitoring data into co-management frameworks, which may promote more sustainable fisheries management strategies in data-poor regions.

New possibilities in antibacterial treatment of intra-abdominal infections caused by multiresistant microflora

To determine the place of new drugs with activity against multidrug resistant strains of microorganisms in the treatment of complicated intraabdominal infections. The incidence and distribution of pathogens isolated from intra-abdominal specimens in patients with intra-abdominal infections are analyzed. The current situation on the growth of resistant strains among pathogens causing intra-abdominal infections is rewied. New combined drugs for the treatment of multidrug resistant infections - ceftolozane/tazobactam and ceftazidim/avibactam plus metronidazole, has been suggested. Their potential role in empiric and targeted antibacterial treatment of complicated intraabdominal infections is defined. Taking into consideration local monitoring data and risk factors of multi resistant strains Ceftolozane/tazobactam in combination with metronidazole can be used in empiric regime of treatment. Due to the high activity on carbapenem resistant strains of Klebsiella pneumonia and the lack of alternatives, it is advisable to use Ceftazidim/avibactam for the targeted therapy.

Environmental Monitoring of Ancient Buildings Based on a Wireless Sensor Network.

Environmental monitoring plays an important role in the preventive protection of ancient buildings, although it is still in the prototype stage. In order to provide data support for the protection of ancient buildings, an environmental monitoring system with multi-sensor and multi-node for the interior and exterior of ancient buildings is designed and realized, based on ZigBee, TCP/IP, and intranet penetration technology. The new type of indoor node package box and outdoor package device are designed to meet the needs of different types of sensors. The monitoring platform is developed on the strength of the LabView so as to obtain real-time display, storage, and over-limit warning functions for local and remote monitoring data. It also proves that the monitoring system is stable and reliable by monitoring the actual ancient building with a brick (stone) structure.

Research on the creep mechanism of Huangniba landslide in the Three Gorges Reservoir Area of China considering the seepage–stress coupling effect

The impoundment of China’s Three Gorges Reservoir has led to the revival of many old landslides along the bank, and the annual fluctuation in water level of 30 m is likely to affect the creep deformation of these landslides. Through a field investigation of Huangniba landslide in the Three Gorges Reservoir Area, the local deformation signs and field monitoring data were analyzed. It was preliminarily considered that this landslide was currently at the creep stage and the creep rate was affected by the reservoir level variation. In order to carry out further research on the effect of reservoir level variation on the landslide movement, the finite element method was used to establish the seepage–stress coupling model of Huangniba landslide based on the seepage–stress coupling theory and taking into account the creep behavior of landslide material. The model was used to simulate the variation characteristics of the seepage field, stress field, and displacement field in the landslide during 6 years of cycles of reservoir level variation. Based on the numerical simulation results and field monitoring data, the deformation mechanism of the landslide was discussed. Finally, the sensitivity of the parameters in the creep model to the simulation results was discussed. The results show that creep deformation is the main deformation mechanism of Huangniba landslide, and it is obviously affected by the drawdown of the reservoir water level. During drawdown of the reservoir level, the pore water pressure inside the landslide decreases, resulting in an increase in effective creep stress and creep strain rate, thus accelerating creep deformation. The results of this study will provide a reference for the deformation mechanism of such creeping landslides subjecting to reservoir water level fluctuation.

Using Earthquakes, T Waves, and Infrasound to Investigate the Eruption of Bogoslof Volcano, Alaska

AbstractThe 2016–2017 eruption of Bogoslof volcano, a submarine stratovolcano in the Bering Sea, produced 70 discrete explosive eruptions over 8 months. With no local monitoring data, activity was seismically recorded on nearby islands 50–100 km away, limiting the detection and resolution of seismic observations. We construct a matched filter catalog of 3,199 events from 49 earthquake families, many of which occurred with hydroacoustic T waves of varying strength. We then use a 2‐D finite difference model to show that hydroacoustic amplitudes should decrease with increased source depth beneath the edifice and leverage each family's seismically recorded T wave amplitude as a proxy for source depth, which we compare to regional infrasound data. This unique combination of using P and S waves to detect events, T waves as a proxy for depth, and infrasound for precise timing of emissions allows us to interpret the dynamics and evolution of the Bogoslof eruption.

Fishery characteristics in two districts of coastal Tanzania

Nearshore marine fisheries provide the main source of protein for nearly 9 million people in coastal villages of Tanzania, yet for decades the fisheries have shown signs of overexploitation. These fisheries are small-scale and co-managed by local coastal communities and governmental authorities in groups known as Beach Management Units (BMUs). BMUs record individual fishing trip data (e.g. gear, vessel, taxa); however, the catch data have only been analyzed in nationally aggregated statistics. The objective of this study was to determine if BMU catch-assessment surveys can provide information on the fishing characteristics of small-scale fishing communities in Tanzania. We collected all available landings data from 2014 to 2017 from BMUs in fourteen villages in two spatially, socially, and ecologically distinct districts (Pangani and Rufiji) of the country. Our results show that each village had unique patterns for vessel-use, gear-use, and taxa landed, and that every village was specialized in some measure. Specifically, two villages in Pangani district landed octopus or parrotfish almost exclusively, suggesting potential trophic cascades after years of overexploitation. Furthermore, village fisheries had shared characteristics within their district, thus describing how fishing patterns vary at multiple spatial scales along the coast. Although imperfect, the catch data collected by the community organizations have generated the first descriptions of how village-based fisheries in Tanzania function. Using these findings, we suggest implementing local monitoring data and analysis into the fisheries management plans at the village and district scale. Continuing to collect and analyze community collected data is necessary to gain insights into the range of characteristics of small-scale fisheries to improve current management programs.

Postearthquake Strength Assessment of Steel Moment-Resisting Frame with Multiple Beam-Column Fractures Using Local Monitoring Data

AbstractQuantitative reports on postearthquake infrastructure condition are valuable to engineers, building owners, local government entities, and the public. Despite their utility, sensor-based pr...