Related Environmental Data Research Articles

Empowering Communities In Transformative Transport Decision-making: A Participatory Spatial Approach to Select Vulnerable Facilities for Citizen Sensing

Introduction/Background This paper presents the first steps of the BEC2SCHOOL project, aimed at providing communities and policy-makers with methods and tools to enhance public participation in transport decision-making and foster the co-design of sustainable mobility solutions, taking advantage of smart technologies and data. Materials and Methods The methodology to select the most suitable facilities to be involved in the project is presented. In particular, it consists of the choice of the criteria to be selected based on different features, such as the presence of infrastructures for cycling and pedestrian mobility and users’ exposure to traffic externalities. These criteria are used for a spatial analysis aimed at identifying the priority of interventions and paving the way for the next steps of the project. Results The main results show that traffic is considered the most important critical issue, and many schools show a medium-high level of vulnerability according to the selected criteria. Discussion Results pave the way for further analyses that are needed to select the most suitable facilities where traffic and environmental sensors will be installed. Conclusion A bottom-up participatory approach will be set up, involving educational facilities and providing their users with tools to collect traffic and environmental-related data, analyze the relevant impacts and propose proper solutions to policy-makers in order to tackle the mobility challenges.

A Review of Sensor-Based Interventions for Supporting Patient Adherence to Inhalation Therapy.

This review aims to provide a comprehensive overview of sensor technologies employed in interventions to enhance patient adherence to inhalation therapy for chronic respiratory diseases, with a particular emphasis on human factors. Sensor-based interventions offer opportunities to improve adherence through monitoring and feedback; however, a deeper understanding of how these technologies interact with patients is essential. We conducted a systematic review by searching online databases, including PubMed, Scopus, Web of Science, Science Direct, and ACM Digital Library, spanning the timeframe from January 2014 to December 2023. Our inclusion criteria focused on studies that employed sensor-based technologies to enhance patient adherence to inhalation therapy. The initial search yielded 1563 results. After a thorough screening process, we selected 37 relevant studies. These sensor-based interventions were organized within a comprehensive HFE framework, including data collection, data processing, system feedback, and system feasibility. The data collection phase comprised person-related, task-related, and physical environment-related data. Various approaches to data processing were employed, encompassing applications for assessing intervention effectiveness, monitoring patient behaviour, and identifying disease risks, while system feedback included reminders and alerts, data visualization, and persuasive features. System feasibility was evaluated based on patient acceptance, usability, and device cost considerations. Sensor-based interventions hold significant promise for improving adherence to inhalation therapy. This review highlights the necessity of an integrated "person-task-physical environment" system to advance future sensor technologies. By capturing comprehensive data on patient health, device usage patterns, and environmental conditions, this approach enables more personalized and effective adherence support. Key recommendations include standardizing data integration protocols, employing advanced algorithms for insights generation, enhancing interactive visual features for accessibility, integrating persuasive design elements to boost engagement, exploring the advantages of conversational agents, and optimizing experience to increase patient acceptance.

Changes of Surface Water Resources in China's Mountains in the Past 50 Years

Mountains, provide crucial water resources for the development of human society, are the water towers of the world. However, mountain water resources have undergone marked changes in recent decades, posing a threat to sustainable human development. Based on long-term hydrological, meteorological, and related environmental data, this study analyzes the evolving patterns of water resources in China's mountains over the past 50 years using trend analysis methods. The results indicate that surface water resources in China's major mountains have undergone noticeable changes over the past 50 years, with varying trends among different mountains. The Qinghai-Tibet Plateau and Tianshan regions show an overall increasing trend in surface water resources, while the Hengduan Mountains, Taihang Mountains, Qinling Mountains, Changbai Mountains, and Yunnan-Guizhou Plateau have witnessed decreasing trends to varying degrees. Furthermore, there are differences in the primary processes influencing surface water resource changes across various mountains. For example, climate change plays a predominant role in driving surface water resource changes in the high-altitude Qinghai-Tibet Plateau and Tianshan region, while human-related activities exert a stronger impact on surface water resource changes in other mountains. To address the challenges brought about by changes in China's mountain surface water resources, it is crucial to take into full consideration the unique natural environments and social systems of each mountain.

Hydropedological digital mapping: machine learning applied to spectral VIS-IR and radiometric data dimensionality reduction

ABSTRACT Pedosphere-hydrosphere interface accounts for the association between soil hydrology and landscape, represented by topographic and Remote Sensing data support and integration. This study aimed to analyze different statistical radiometric and spectral data selection methods and dimensionality reduce environment-related data to support the classification of soil physical-hydric properties, such as soil basic infiltration rate (bir) and saturated hydraulic conductivity (Ksat); as well as to act in data mining processes applied to hydropedological properties digital mapping. Accordingly, research integrated information from Visible to Infrared (VIS-IR) spectral indices and Sentinel’s 2A mission Multispectral Instrument (MSI) sensor bands, terrain numerical modeling and aerogeophysics set to model soil-water content in two soil layers (0.00-0.20 m and 0.20-0.40 m). Pre-processed data were subjected to statistical analysis (multivariate and hypothesis tests); subsequently, the methods were applied (variation inflation factor - VIF, Stepwise Akaike information criterion – Stepwise AIC, and recursive feature elimination - RFE) to mine covariates used for Random Forest modeling. Based on the results, there were distinctions and singularities in spectral and radiometric data selection for each adopted method; the importance degree, and contribution of each one to soil physical-hydric properties have varied. According to the applied statistical metrics and decision-making criteria (highest R 2 and lowest RMSE / MAE), the chosen methods were RFE (0.00-0.20 m layers) and Stepwise AIC (0.20-0.40 m layers) - both concerned with the assessed variables (bir and Ksat). This approach captured the importance of environmental variables and highlighted their potential use in hydropedological digital mapping at Guapi-Macacu watershed.

Combined impacts of environmental and socioeconomic covariates on HFMD risk in China: A spatiotemporal heterogeneous perspective.

Understanding geospatial impacts of multi-sourced influencing factors on the epidemic of hand-foot-and-mouth disease (HFMD) is of great significance for formulating disease control policies tailored to regional-specific needs, yet the knowledge is very limited. We aim to identify and further quantify the spatiotemporal heterogeneous effects of environmental and socioeconomic factors on HFMD dynamics. We collected monthly province-level HFMD incidence and related environmental and socioeconomic data in China during 2009-2018. Hierarchical Bayesian models were constructed to investigate the spatiotemporal relationships between regional HFMD and various covariates: linear and nonlinear effects for environmental covariates, and linear effects for socioeconomic covariates. The spatiotemporal distribution of HFMD cases was highly heterogeneous, indicated by the Lorenz curves and the corresponding Gini indices. The peak time (R2 = 0.65, P = 0.009), annual amplitude (R2 = 0.94, P<0.001), and semi-annual periodicity contribution (R2 = 0.88, P<0.001) displayed marked latitudinal gradients in Central China region. The most likely cluster areas for HFMD were located in south China (Guangdong, Guangxi, Hunan, Hainan) from April 2013 to October 2017. The Bayesian models achieved the best predictive performance (R2 = 0.87, P<0.001). We found significant nonlinear associations between monthly average temperature, relative humidity, normalized difference vegetation index and HFMD transmission. Besides, population density (RR = 1.261; 95%CI, 1.169-1.353), birth rate (RR = 1.058; 95%CI, 1.025-1.090), real GDP per capita (RR = 1.163; 95%CI, 1.033-1.310) and school vacation (RR = 0.507; 95%CI, 0.459-0.559) were identified to have positive or negative effects on HFMD respectively. Our model could successfully predict months with HFMD outbreaks versus non-outbreaks in provinces of China from Jan 2009 to Dec 2018. Our study highlights the importance of refined spatial and temporal data, as well as environmental and socioeconomic information, on HFMD transmission dynamics. The spatiotemporal analysis framework may provide insights into adjusting regional interventions to local conditions and temporal variations in broader natural and social sciences.

Spatiotemporal dynamics and potential restoration of mangroves in Circum-Xinying-Bay region, Hainan Province, China

Mangrove forests play an important role in absorbing carbon, filtering pollution, and protecting coastal residents from storms. However, mangrove forests are most susceptible to human interference and suffer more than one-third of losses globally. Therefore, quantifying their spatial-temporal changes and identifying their potential distributions are necessary for protection, restoration, and management. We first mapped and analysed the spatial-temporal changes in mangrove forests from 1993 to 2021 in the Xinying-Bay region (XYB), Hainan Province, China. Then, we predicted suitable habitats for mangrove forests by using the maximum entropy (MaxEnt) model based on mangrove distribution data for 2021 and related environmental data for the Circum-Xinying-Bay region (CXYB). The results show remarkable changes in mangrove forests in the XYB from 1993 to 2021, with the loss rate reaching 56 ha/y from 1998 to 2002. The MaxEnt model performs well, with the curve (AUC) extending beyond 0.9. Water quality and topography determined the distribution of mangrove forests, with the total contribution exceeding 65%. The six most important influential factors were salinity, DEM, precipitation in the driest quarter, pH, isothermality, and winter sea surface temperature. The area of mangroves highly suitable in the CXYB is approximately 3880 hm2 and is mainly located in the town of Xinzhou, the northeast area of the town of Mutang and the southwest area of the town of E'man. This suggests that areas prioritized for protection should include the northern and western areas of Xinzhou, northeastern areas of Mutang, and northern and western areas of E'man. These findings provide further understanding of mangrove distribution and can guide mangrove restoration and management.

Construction of Urban Environmental Performance Evaluation System Based on Multivariate System Theory and Comparative Analysis: A Case Study of Chengdu-Chongqing Twin Cities, China.

Based on the related environmental data of Chengdu and Chongqing from 2011 to 2020, this paper constructs a multivariate environment performance evaluation system, combines the self-built indicator system determination criteria and rules, evaluates and compares the environmental performance of Chengdu and Chongqing, and also discusses the impact of COVID-19 on urban environmental performance. The research results show that the overall environmental performance increased from 2011 to 2020, but there are differences between different subsystems, mainly manifested in the best water environment performance, followed by air environment and solid waste; moreover, the noise environment maintains a relatively stable level. By comparing the average levels of various subsystems of the Chengdu-Chongqing dual cities from 2011 to 2020, it can be seen that Chengdu City has better environmental performance in air environment and solid waste, while Chongqing City has better environmental performance in the water environment and noise environment. In addition, this paper also found that the impact of the epidemic on urban environmental performance mainly comes from the impact on the air environment. At present, the overall environmental performance of the two places has shown a trend of environmentally coordinated development. In the future, Chengdu and Chongqing should further optimize and improve their relatively weak environmental subsystems, deepen the joint action mechanism between the two places, and build a green and high-quality development economic circle for the Chengdu-Chongqing twin cities.

The role and value of data in advancing environmental sustainability with empirical evidence from tissue and textile businesses

As the need for genuine sustainability transformation crystallises, data’s digitalisation-enabled use creates opportunities to refine how businesses and societies operate. With the power of diverse data sources, available in increasing quantity, actors can set feasible environmental targets, identify improvement opportunities, implement actions, and follow development. The dissertation explores data’s role and value for corporate environmental sustainability and specifically for circular economy from four perspectives: the customer-perceived value of data for environmental sustainability (considered via studying a tissue-paper supplier), how data’ use influences sustainability pathways toward circular economy (specifically of textiles), paradoxical tensions that arise in utilising data to drive circular economy (in the textiles context), and how the literature characterises data’s role and value in circular business models. A company case study, disaggregative Delphi and literature review were used as research methods. The work pinpointed availability of detailed, reliable product-specific data as crucial for supporting environmental sustainability and transparency of products’ value chains. Also, capturing data’s value here demands collaboration with customers and suppliers but also wider business networks, central to which are conditions for solid data-sharing. Consumers demand environmental sustainability, circularity, and accrue benefit from data-related decisions on individual purchases. The strategic and operative decisions/activities across business functions and throughout value chains further guide environmentally better-informed decisions. While use of data can be crucial to developing sustainable business models (for circularity specifically), the role varies less between models than with the activity supported. The results clearly implies that neither environment-related data nor initiatives utilising data automatically benefit the environment; the interactions are more complex. Still, the data are a critical business enabler, and anticipating future data needs should dovetail with systematically developing environment-related data-management capabilities. These findings provide rich insight as to the elements, mechanisms, and critical issues of data driving environmental sustainability and circular economy.

Demand-Supply Gap in Energy Use in BRICS Countries with Special Reference to India - Emerging Challenges and Policies

Brazil, Russia, India, China, and South Africa (BRICS) Energy Research Co-operation Platform (ERCP) is an international program for developing energy-based sustainable development and sharing advanced energy technologies. BRICS is a heterogeneous club of nations having around a quarter of GDP, supplyling energy products to 40 percent of the world's population. The central objectives of the study are (a) the overall production consumption gap in energy use, (b) details of energy production, consumption; total proved reserves, import, export of energy, growth rate of energy consumption, (c) associations among energy use, demographic, socio-economic and environmental aspects and (d) policy measures undertaken to reduce energy gap along with pathways to the low-carbon environment. The study is based on the secondary data on (a) energy proved reserves, production and consumption for the period 2011-2021, (b) associated demographic, socio-economic, and environment related data obtained from different reports and others. Results have been presented through tabular forms and through figures, and percentages. Among BRICS countries, there are heterogeneous production-consumption gaps for different sources of energy, particularly oil.

Soil-transmitted helminths detected from environmental samples in a campus of southern Brazil

Soil harbours enormous biodiversity, essential for maintaining environmental and human health. However, soil can also be a reservoir of various parasitic pathogens, such as soil-transmitted helminths (STH). We evaluated the presence of STH (e.g., hookworms, roundworms and whipworms) in soil samples collected at twenty points within the perimeter of Campus do Vale (a university campus belonging to the Federal University of Rio Grande do Sul - UFRGS), during 2022 winter season. Considering the One Health perspective, human, animal and environment-related data from each sampling point were collected. All soil samples showed nematode larvae, representing natural components of soil biodiversity. Considering STH eggs, 35% (n = 7) of soil samples showed hookworm eggs (e.g., from Necator americanus or Ancylostoma duodenale), 10% (n = 2) showed roundworm (Ascaris lumbricoides) eggs, and 5% (n = 1) showed whipworm (Trichuris trichiura-like) eggs. Of note, 10% of the sampling points showed the presence of rhabditiform hookworm larvae, 5% showed Strongyloides stercoralis rhabditiform larvae and 5% had the presence of filariform hookworm larvae, indicating a risk of human percutaneous infection. The significant people circulation in Campus do Vale, in association with other environment-related factors, help to explain the prevalence of STH observed in this study.

Effect of environment and season on acute decompensated heart failure: Data from low-to middle-income country

ObjectivesThe environmental effect in heart failure (HF) patients is well established. However, the data is limited from low-to middle-income countries like India. This study determined the impact of environment on acute decompensated HF (ADHF) admissions and mortality in India. MethodsRetrospectively, the data of all HF patients admitted between April 2017 and March 2019 was accessed through electronic hospital records. Simultaneously, the environmental-related data was collected from the central pollution control board. ResultsThe study included 4561 patients of ADHF. The peak of monthly ADHF events (admission and mortality) was observed during the chilly month (January) while the lowest rates were observed in summer months (May–June). The most significant factor correlating inversely with the monthly ADHF admission (r = −0.78, p = 0.003) and mortality (r = −0.65, p = 0.004) was the maximum air temperature, and it was found to be the independent predictor for both ADHF mortality [t = −2.78, β = −0.84; 95%CI(-6.0 to −0.6), p = 0.021] and admission [t = −4.83, β = −0.91; 95%CI(-19.8 to −6.9), p = 0.001]. The above correlation was better seen in the elderly subset and male gender. Humidity and the air pollution attributes did not have a significant correlation with ADHF admission or mortality. ConclusionIn conclusion, even in low-to middle-income country like India, a periodic effect of season was demonstrated for ADHF mortality and admission, with a peak in ADHF events noted during winter months especially in the regions having extremes of seasons. Air pollution could not affect the ADHF outcome for which further studies are needed.

Unsupervised neural network-enabled spatial-temporal analytics for data authenticity under environmental smart reporting system

Environment, social and governance (ESG) disclosures required on listed companies have aroused considerable interest in both academia and industry for sustainable development and investing. However, the authenticity and credibility of the ESG report exposed to the public remain in doubt due to black box-like reporting processes with massive human involvements. In this study, a framework of environmental smart reporting system (BI-ESRS) based on the blockchain and Internet of Things (IoT) technologies is developed to automate the acquisition of environment-related data and make the reporting reliable and traceable. In addition, we evaluate the authenticity of the data collected from IoT devices, considering human-made counterfeits on measuring instruments for greenwashing. It is anticipated to stimulate companies to submit high-quality data without fake. Specifically, an unsupervised neural network-enabled spatial-temporal analytics (UN-STA) method is devised to achieve anomalies detection and index the data with an authenticity rate. An artificial neural network, self-organizing map (SOM), is applied to construct the prediction model. The received signal strength indicator (RSSI) of Bluetooth low energy (BLE), the vibration amplitude of smart instruments and data uploading interval constitute the input vector for competitive learning. Finally, an experimental simulation is carried out to demonstrate the implementation of the proposed system and method, and their effectiveness has also been testified. Moreover, the sensitivity of the SOM model over the three factors has been analyzed by applying the control variate technique. This work is expected to serve as a reference for practitioners to satisfy similar requirements in the industry and inspire new ideas for scholars.

Governing the Environment-Related Data Space

Governing the Environment-Related Data Space

Using Sensor Network for Tracing and Locating Air Pollution Sources

Atmospheric fine particulate matter (particulate matter with an aerodynamic diameter ≤ 2.5 μm in ambient air; PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> ) is a major pollutant causing regional air pollution and harm to human health. To monitor PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , Chinese industry authorities use data from a small number of fixed stations with uneven distribution. Pollution control is promoted through assessment and enforcement, which can include industry-wide shutdown measures harmful to local economic development. The objective of this study was to establish a fine particulate matter network (FPMN) of sensors based on the Internet of Things with low cost, high spatiotemporal resolution, flexible distribution points, large numbers, and high collection frequency. The FPMN-derived data, together with other multisource environment-related data, could be used to track and locate atmospheric pollutants and selectively identify source. This study adopted Chizhou, China as the research object. Specifically, the work included designing the FPMN, selecting locations for sensor placement on the basis of local weather, terrain, and land use, and using software/hardware collaborative calibration technology to ensure consistency between FPMN-derived data and National Control Station(NCS) data. The analysis revealed that FPMN data effectively reconstructed a reliable regional field of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration with improved spatiotemporal accuracy. The research results will have great importance regarding the traceability of sources of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> pollution, analysis of pollution causes and transboundary pollution, optimization and adjustment of industrial layouts, and differentiation of the temporal and spatial control of pollution. Ultimately, the FPMN could directly support management and decision-making processes of local governments in relation to the environment and industry.

Research on main transformer defect detection methods based on Conditional Inference Tree and AdaBoost Algorithm

With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.

An Environmental Data Collection for COVID-19 Pandemic Research

The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.

Interaction between economic growth energy consumption and environmental pollution

A large amount of energy consumption promotes rapid economic growth and brings serious environmental pollution problems. The purpose of this paper is to study the dynamic interaction between economic growth, energy consumption, and environmental pollution. This paper selects the 2003-2016 A-provincial time data as the research object, and constructs a vector autoregressive model based on economic, energy and environmental related data. The Johansen cointegration test, Granger causality analysis, IRF impulse response and variance decomposition method were used to empirically analyse the dynamic interaction between energy consumption, economic growth and environmental pollution. The results show that there is a long-term equilibrium relationship between energy consumption, environmental pollution and economic development. Economic growth and energy consumption are the Granger causality of environmental pollution. The positive impact of energy consumption on economic growth is long and stable. The interpretation of energy consumption on environmental pollution is 44.2%, indicating that environmental pollution is more affected by changes in energy consumption.

Utilization of New Technologies in the Production of Pharmaceutical Olive Oil.

Olive oil is a key ingredient in the Mediterranean diet and offers many health benefits. However, many factors affect the quality and quantity of olive oil such as olive tree diseases and olive-related pests. Unfortunately, the procedure of identifying pests or the outbreak of a disease is time-consuming, and it depends heavily on the size of the olive grove. Through the use of ICT, remote monitoring of the olive grove can be achieved, by collecting environment-related data and having an overview of the olive grove's overall health. In this paper we propose a low-cost dense network of sensors that collects daily data regarding the olive grove, thus, providing the possibility to prevent infestation of olive fruit fly and/or the outbreak of olive tree-related disease.

Historical human activities accelerated climate-driven desertification in China’s Mu Us Desert

China’s Mu Us Desert, located in an energy-rich strategic base of the northwestern Loess Plateau, has acted as a crucial agro-pastoral transition zone for thousands of years. However, the area experienced notable climate and environmental change from 221 BCE to CE 907 (1128 years), which may have profoundly affected its landscape evolution up to modern times. To explore this process and associated driving mechanisms, we conducted a comprehensive study based on a dataset of 882 human archaeological sites (HASs), historical documents, and related environmental data of the Mu Us Desert and its surrounding area (MUDISA). We found that the MUDISA experienced large-scale immigration and an agricultural boom (790 HASs) during the Qin and Han dynasties (221 BCE–CE 220). This coincided with an ecologically favorable environment and may have potentially disturbed the desert’s eco-environmental equilibrium. Coinciding with the deteriorating natural conditions, the MUDISA’s animal husbandry replaced agriculture as the dominant subsistence economy during the era of disunity (CE 220–581, 33 HASs) and during the Sui and Tang dynasties (CE 581–907, 59 HASs). Although natural and political-economic factors together caused a sharp decline in number and scattering for HASs in the desert, people and livestock were widely distributed and maintained a high population level. This situation, coupled with the dry climate and fragile geographical environment, may have significantly accelerated the climate-driven desertification process from CE 220 to CE 907. This study highlights the long-term human–nature relationship and its impact on historical desertification in the Mu Us Desert, and may shed new light on historical environmental change in arid and/or semi-arid areas in northwest China and globally.

What influences the establishment and functioning of community-based monitoring initiatives of water and environment? A conceptual framework

Community-based monitoring of water and environmental resources is believed to have the potential to help produce more or better water and environmental data, increase public participation in environmental monitoring, respond to issues of common community concern and enhance informed, democratic and transparent environmental decision making. Despite these perceived outcomes, initial and long term engagement of different stakeholders with community-based monitoring activities remains a challenge. Successful establishment of these initiatives has proven to be complex and there is little evidence that community-based monitoring projects have an impact on environmental or water resources management processes, or a legacy beyond their funding period. Whereas much attention is being paid to promoting community-based monitoring as a tool for producing more or better water or environment-related data, there is little conceptual understanding of how to critically analyze and understand the features and functioning of these initiatives. Therefore we do not know whether, or to what extent, these initiatives deliver on their perceived potentials. Thus, it is essential to learn from past experiences and critically document, analyze and understand factors that might influence the establishment, functioning and outcomes of community-based monitoring initiatives. Based on the review of a large body of literature in the fields of community-based monitoring, Citizen Science and affiliated fields of research, combined with the empirical evidence from a number of major EU-funded community-based monitoring projects, this paper introduces a framework that can be utilized to analyze the dynamics underlying the establishment and functioning of community-based monitoring initiatives. The distinction between five different dimensions, consisting of 22 internal and context-related factors, is a unique feature of this framework that broadens its applicability and makes it suitable for ‘Context analysis’, ‘Process evaluation’ and ‘Impact assessment’ of the initiatives. The introduced framework is therefore called the CPI framework in short.