Environmental Monitoring Data Research Articles

Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress

We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.

CTBT data for environmental radiological monitoring: a preliminary study in Malaysia

CTBT data for environmental radiological monitoring: a preliminary study in Malaysia

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring.

Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.

Morphological, cytological, physiological, and molecular evidence to explain how underground fleshy roots expand in herbaceous peony (Paeonia lactiflora Pall.)

The fleshy roots of herbaceous peonies serve as vital storage organs and are the primary source material for Chinese herbal medicine. Moreover, in certain cultivars, these roots can be propagated by root cuttings. However, the specific mechanisms governing the expansion and development of fleshy roots in herbaceous peonies currently remain unclear. To elucidate patterns of changes during the enlargement and development of herbaceous peony fleshy roots, this study assessed fleshy roots in six growth and development periods (P1–P6). By integrating environmental monitoring data, morphological, cytological, physiological and biochemical, as well as molecular characteristics across these developmental periods were compared. Both precipitation and temperature influenced the development of fleshy roots. The fastest enlargement of fleshy roots occurred under the following growth conditions: daily average precipitation = 5.6 mm; monthly average air temperature = 25.6°C; monthly average soil temperature = 26.3°C. Phloem and xylem, produced by the activity of vascular cambium, are important components of fleshy roots. Secondary xylem thickness consistently increased across the six developmental periods, resulting in the radial expansion of fleshy roots. When average monthly air and soil temperatures were about 26°C, starch accumulated gradually in fleshy roots, and carbohydrates were mostly starch-based. However, when the average monthly air and soil temperatures decreased to about 11.0°C, soluble sugar content increased. Endogenous hormones such as IAA, ZR, GA3, MeJA, and ABA played regulatory roles throughout the development of fleshy roots, and their levels and dynamic equilibrium were correlated with expansion rate. Elevated IAA and ZR content, and stabilized IAA/ABA and MeJA/ABA ratios, favored fleshy root expansion. Trends in the expression of three genes that play key roles in the expansion of fleshy roots (PlEXLB3, PlAUX1 and PlWRKY13) were consistent with morphological and physiological data, but PlHXK1 and PlIPT3 also play important roles. These findings lay a foundation for studying, in greater depth, the regulatory mechanisms governing the enlargement of herbaceous peony fleshy roots, and offer practical advice pertaining to field cultivation and management practices of this ornamental plant.

Recent-year variations in O3 pollution with high-temperature suppression over central China

By analyzing environmental and meteorological monitoring data over recent years of 2015–2022, the Twain-Hu Basin (THB) in central China was identified as a regional O3 pollution center over China with the highest increasing trend at 1.10 %⸱yr−1 in interannual variations of O3 concentrations with deteriorating O3 pollution over recent years. We explored the spatiotemporal variations in O3 pollution in the THB with ozone suppression (OS) under high air temperature over metropolitan, small urban, and mountainous areas. The bipolarized interannual trends in interannual O3 variations in urban and mountainous areas over central China were characterized with the increasing and decreasing 90th percentiles of the daily maximum 8-h (MDA8-90) O3 concentrations respectively in polluted urban areas and clean mountainous areas over recent eight years. The changes of the near-surface O3 concentrations with air temperature exhibited the inflection points of OS from increasing to decreasing O3 at air temperature of 30.5 °C in mountainous areas, 32.5 °C in small urban areas, and 34.5 °C in metropolitan areas, and the intensity of OS was estimated in the ranking with mountainous areas (−2.30 μg⸱m−3⸱°C−1) > small urban areas (−1.96 μg⸱m−3⸱°C−1) > metropolitan areas (−1.54 μg⸱m−3⸱°C−1), indicating that the OS was more significant over the lower-O3 mountainous areas. This study has implications for understanding O3 pollution variations with the meteorological drivers.

An integrated system of energy generation, storages, and appliances consumption based on machine learning techniques and internet of things

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Sharing instant delivery UAVs for crowdsensing: A data-driven performance study

In recent years, there has been a significant increase in demand for instant deliveries, such as rapid delivery of takeaway food and medicine. Many logistics companies are planning to realize real-time delivery services through unmanned aerial vehicles (UAVs). However, costs of running such an autonomous delivery system are too expensive. Fortunately, urban management departments (UMD) that are responsible for monitoring urban environments have the intentions to cooperate with external companies and design crowdsensing methods, so that the duty can be ensured in an autonomous and low-cost method. Motivated by this, instant delivery UAVs are introduced into crowdsensing, so that UAVs can earn additional money for logistics companies while ensuring priority completion of instant deliveries, as well as provide environmental monitoring data to UMD. To be specific, some challenges are firstly identified to combine these two tasks based on real-world data in China. Then a set of data-driven UAVs sharing algorithms is proposed to achieve sensing for urban POIs (Points of Interest) and urban anomaly events. The results of simulations based on real-world datasets demonstrate the efficiency of our methods. The average sensing interval of the POIs within a 368.64π km2 area can be only ≈2.6 min. more than 81% of stochastic events are successfully predicted, over 84% (401 out of 476) of events are successfully sensed, and 60% of events have a UAV to arrive within 6.65min of their occurrence.

Automated system for intertidal mollusk keeping with tidal cycle imitation

The article describes a working prototype of an automated laboratory setup for intertidal mollusk keeping with tidal cycle imitation. The setup currently consists of two groups of five aquariums. At high tide, water is gravity fed into the aquariums from a common water tank; at low tide, water is gravity drained into a common collection tank. Water is transferred between the tanks using a special pump. The control unit consists of a networked Arduino Mega board and two Arduino Nano boards. The Arduino Mega board is responsible for the environmental monitoring systems, data logging, and automatic seawater change, as well as a software-based selfchecking and decision-making system. One of the Arduino Nano boards is responsible for realizing the tidal cycle; the other board is responsible for the remote monitoring and control of the setup via SMS. The results of pilot experiments indicate a significant reduction in labor costs in mollusk keeping. The developed setup can be used to keep a wide range of intertidal animals for studying their biology, assessing the impact of environmental factors, and biotesting, as well as for educational purposes.

Analysis of the Jumping Characteristics and Influencing Factors of Ozone Pollution in Beijing

Based on environmental monitoring data and meteorological observation data from 2016 to 2022 in Beijing, combined with backward trajectory clustering and potential source area contribution analysis, the characteristics, meteorological impacts, and potential source areas of ozone (O3) pollution were analyzed. The results showed that there was a total of 41 O3 pollution processes with jumping characteristics in Beijing from 2016 to 2022, with an average of 5.9 times a year. The occurrence time was concentrated in May to July, and the day of the jump (OJD2) was higher than the day before the jump (OJD1). The average value of ρ(O3-8h) was 78.3% higher, and the peak concentration was 78.9% higher. The high O3 concentration zone in the OJD2 region exhibited a characteristic of advancing from south to north. The main reasons for the occurrence of jumped O3 pollution in Beijing could be summarized as local accumulation caused by unfavorable meteorological conditions and regional transmission impact. The occurrence of jump-type ozone pollution was characterized by an increase in southerly wind frequency, temperature rise, pressure decrease, and precipitation decrease. The increase in southerly wind frequency provided conditions for the transport of O3 and its precursors, and rapid photochemical reactions occurred under local high temperatures, with less superimposed precipitation, comprehensively pushing up the ozone concentration level of OJD2. Six air mass transporting pathways were identified through clustering analysis; the air mass from the direction north of OJD2 decreased by 11.2%, whereas the air mass from the south and east directions increased by 6.7% and 4.4%, respectively, with the air masses mainly transmitting over short distances. The ozone concentration corresponding to the south and east directions was relatively high, making a significant contribution to Beijing's pollution. The analysis of potential source areas revealed that the main potential source areas of OJD2 ozone pollution were the central, southern, and eastern parts of Beijing-Tianjin-Hebei, which contributed 82.6% to the pollution trajectory. There was a significant contribution of regional transport during jump-type ozone pollution, and it is necessary to strengthen joint prevention and control in the Beijing-Tianjin-Hebei Region.

A Siamese Swin-Unet for image change detection

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

РОЛЬ ГОСУДАРСТВЕННОГО МОНИТОРИНГА ОКРУЖАЮЩЕЙ СРЕДЫ В ЭКОЛОГИЧЕСКОЙ ОЦЕНКЕ ОСОБО ОХРАНЯЕМЫХ ПРИРОДНЫХ ТЕРРИТОРИЙ

In the article, the authors examined the role of specially protected natural territories in the system of nature management. Summarizing domestic and foreign experience, it is possible to give a comprehensive description of specially protected natural areas, in the table. They clarified the theoretical and methodological foundations of the management of specially protected natural territories, which include the ecological and economic essence of specially protected natural territories. In their study, the authors paid special attention to the environmental assessment of specially protected natural areas based on environmental monitoring data. The informational role of environmental monitoring for the management of specially protected natural territories is revealed. To select the most rational management strategy for the eco-oriented development of specially protected natural areas, a SWOT analysis was used to identify weaknesses and strengths. Conclusions were drawn about the use of environmental assessment for the development of an environmental reference strategy.

C2A-DC: A context-aware adaptive data cube framework for environmental monitoring and climate change crisis management

A context-aware adaptive data cube (C2A-DC) framework based on Earth Observation (EO) data for environmental monitoring to mitigate Climate Change (CC) effects is proposed. It has the property of combining DC formation, calculation of Remote Sensing (RS) operations, deep and machine learning algorithms for classification and data harmonization by updating the layers of the DC using information obtained from the previous operations, all applied to EO data originating from a satellite selected by a stakeholder. Moreover, the proposed framework is context-aware adaptive in the sense that it allows for environmental monitoring tasks according to a stakeholder’s needs, which is in contrast to existing works being constrained to a single type of environmental monitoring. Furthermore, with the proposed framework end-users do not have to make any additional processing action to handle the data as they are given to them as a set of analysis-ready data, harmonically aligned under a unique DC. To showcase the C2A-DC framework’s effectiveness and applicability in environmental monitoring, crisis management, and post-disaster assessment, it is applied to two real-world cases. The first one corresponds to cloud and shadow detection in a flood event, with several Convolutional Neural Network (CNN) architectures being trained on the C2S-MS floods dataset for classification, while k-means clustering and RS product calculations are used for monitoring scenery changes. The second one corresponds to deforestation monitoring from fires, evaluated using RS products.

Environmental data monitoring and infection risks in UK care-homes in the context of COVID-19

The COVID-19 pandemic drew attention to the critical role of building ventilation as a measure for controlling infection transmission. With the substantial number of COVID-19 outbreaks in care homes worldwide, the effectiveness of ventilation is an important consideration for infection control and wider exposure to indoor air pollutants. In this study, we used IoT-based sensors in two residential care homes to evaluate ventilation in various areas, including bedrooms, corridors, and communal spaces. Our monitoring focused on carbon dioxide (CO2) levels as a proxy for ventilation, as well as temperature and humidity, during the spring of 2022. We also developed a ventilation model using the software CONTAM and coupled it with an infection risk model to assess airborne transmission risks under different weather and occupancy conditions. Our results suggest that ventilation is generally adequate based on UK COVID-19 guidelines at the time, with CO2 below 800 ppm for the majority of the time, and opening windows in communal spaces in elderly care environments can help preserve indoor ventilation during periods of high occupancy. However, modelling data suggests that low CO2 values may be indicative of low occupancy in many spaces and therefore ventilation rates may not be sufficient to mitigate infection transmission. Encouraging positive ventilation behaviours in staff and residents, potentially supported by visible CO2 monitors, and taking additional precautions such as using air cleaners, enabling additional window openings or staff wearing masks during outbreaks and periods of high disease prevalence is likely to be beneficial for resident and staff health.

Alternative approach to evaluate the data of ecological monitoring of ecotopes for highways in Ukraine

We propose alternative approach to evaluate data from ecological monitoring of ecotopes along the roadways of Ukraine, taking into account raised accuracy of treating the ecological impact on the roadside environment. Methodology includes sources analysis, development of a workflow model for monitoring and identifying the main components of input data and their optimal features by system analysis. The monitoring of ecotopes of green plantations along highways in the steppe zone of South Ukraine increases the accuracy of evaluating the ecological trace on roadside environment and facilities. Scientific novelty of study combines various data sampling, physical equipment, and road measurements with information services (computing, analyzing and treating input data, forecasting of system development, and monitoring management through feedback control). The system analysis of data deals with the optimal linear constraints of the roadside ecotope capacity, leading to a simple mathematical model of the monitoring. The problem of processing environmental monitoring data with such data is reduced to the linear programming problem. Specifically, this mathematical model allows for the consideration few factors influencing the ecological situation and provides the ability to analyze and forecast the development of ecosystems along roads. Conclusions of study are criteria for an objective assessment of the degree of pollution of the environment and its impact on soil cover, vegetation and road equipment. For effective monitoring it is necessary data-based quality management system on highways. It helps evaluate the predicted degree of environmental pollution by degradation of soils, vegetation, and road facilities.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.

Satellite Remote Sensing and Geographic Information System Technologies for Industrial Tree Plantation Mapping and Monitoring: A Way Forward for the Sustainable Development of the Philippine ITP Industry

KEY POINTS
 ● Industrial Tree Plantations (ITPs) are a vital contributor to the Philippines' log and timber production, but these ITPs requires better and more efficient way of characterization and monitoring.
 ● Current policies require ground validations surveys to monitor ITPs for regulation purposes. However, these approaches are often difficult to conduct, time consuming and expensive.
 ● Satellite remote sensing (SRS) and Geographic Information System (GIS) technologies offer an efficient alternative to traditional ground surveys for mapping and monitoring ITPs.
 ● SRS and GIS enable detailed mapping of ITPs, including species-level identification, and provides valuable data for forest resource management and environmental monitoring.
 ● SRS and GIS technologies should be integrated into existing policies by the Department of Environment and Natural Resources (DENR) to efficiently map, monitor, and manage ITPs in the country, ensuring informed decision-making and sustainable forestry practices.

On the formation and development of the State environmental monitoring system (State environmental monitoring)

The article talks about scientific and methodological issues of the development of state environmental monitoring. The authors have considered the structure of State environmental monitoring (State environmental monitoring) and the procedure for its implementation. The legal basis of state environmental monitoring systems.
 We present the work within the Framework of the national program of the unified system of state environmental monitoring (State environmental monitoring, SEM). The goals, objectives and procedure for the implementation of the SEM, its main subsystems.
 The scientists examined the role of the Ministry of Natural Resources and Ecology of the Russian Federation (Ministry of Natural Resources of Russia) and the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) in ensuring the functioning of state environmental monitoring and its information resources. The activities of the State Environmental Monitoring Data Fund (State environmental monitoring) and the Unified State Data Fund (USDF) are presented. The researchers have the work of the monitoring system using the state system of observations and control by Roshydromet of indicators of the content of pollutants in atmospheric air, surface waters, marine waters, soil, radiation monitoring, etc.
 The authors have considered the prospects for the development of state environmental monitoring in connection with the adoption by the Government of the Russian Federation of the State program "Environmental Protection" for 2012–2030.
 Ethics. This study did not require the conclusion of the Ethics Committee.

Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang–Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Application of Multi-Data Fusion Technology in Intelligent Design of Urban Environment

Abstract This paper adopts multi-data fusion technology to design urban environmental intelligence. First, store the information collected by the reader communication module and receive and analyze the urban environmental monitoring data sent by the handheld RF reader. Secondly, calculate the mean value of environmental data by weighted average, summarize the sample information by combining it with Bayesian estimation, and fuse the values of the multi-sensor data. Finally, D-S evidence inference is utilized to expand Bayes and enhance the degree of trust in the focal element. The results show that the utilization value of multi-data fusion technology can reach 1000, with the growth of time, the public urban environment concern reaches 900 times, the level of intelligent design of the urban environment is improved faster, and the overall effect is good.