Found 9941 publications. Showing page 375 of 398:
Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 and to the annual hourly concentration in 2019 over the Oslo metropolitan region (1026 ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.
Cambridge University Press
2024
The report presents interim 2023 maps for PM10 annual average, PM2.5 annual average, O3 indicator peak season average of maximum daily 8-hour means, and NO2 annual average. The maps have been produced based on the 2023 non-validated E2a (UTD) data of the AQ e-reporting database, the CAMS Ensemble Forecast modelling data and other supplementary data. Together with the concentration maps, the inter-annual differences between 5-year average 2018-2022 and 2023 are presented (using the 2018-2022 regular and the 2023 interim maps), as well as basic exposure estimates based on the interim maps.
ETC/HE
2024
Transboundary particulate matter, photo-oxidants, acidifying and eutrophying components
Norwegian Meteorological Institute
2024
2024
Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications
Low-cost air quality sensor systems (LCS) are emerging technologies for policy-relevant air quality analysis, including pollution levels, source identification, and forecasting. This report discusses LCS use in networks and alongside other data sources for comprehensive air quality applications, complementing other WMO publications on LCS operating principles, calibration, performance assessment, and data communication.
The LCS’s utility lies in their ability to provide new insights into air quality that existing data sources may not offer. While LCS data must be verified, their integration with other data sources can enhance understanding and management of air quality. In areas without reference-grade monitors, LCS can identify factors affecting local air quality and guide future monitoring efforts. Combining LCS data with satellite and other air quality systems can improve data reliability and establish corroborating evidence for observed trends. LCS can extend the spatial coverage of existing monitoring networks, offering localized insights and supporting effective air quality management policies. Co-locating LCS with reference-grade monitors helps quantify measurement uncertainties and apply LCS data appropriately for forecasting, source impact analysis, and community engagement.
World Meteorological Organization
2024
2024
A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements
Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.
MDPI
2024
2024
To achieve the objectives of COP28 for transitioning away from fossil fuels and phasing these out, both natural and technological solutions are essential, necessitating a step-change in how we implement social innovation. Given the significant CO2 emissions produced by the building sector, there is an urgent need for a transformative shift towards a net-zero building stock by mid-century. This transition to zero-energy and zero-emission buildings is difficult due to complex processes and substantial costs. Building integrated photovoltaics (BIPV) offers a promising solution due to the benefits of enhanced energy efficiency and electricity production. The availability of roof and façade space in offices and other types of buildings, especially in large cities, permits photovoltaic integration in both opaque and transparent surfaces. This study investigates the synergistic relationship between solar conversion technologies and nature-based components. Through a meta-analysis of peer-reviewed literature and critical assessment, effective BIPVs with greenery (BIPVGREEN) combinations suitable for various climatic zones are identified. The results highlight the multi-faceted benefits of this integration across a range of techno-economic and social criteria and underscore the feasibility of up-scaling these solutions for broader deployment. Applying a SWOT analysis approach, the internal strengths and weaknesses, as well as the external opportunities and threats for BIPVGREEN deployment, are investigated. The analysis reveals key drivers of synergistic effects and multi-benefits, while also addressing the challenges associated with optimizing performance and reducing investment costs. The strengths of BIPVGREEN in terms of energy efficiency and sustainable decarbonization, along with its potential to mitigate urban and climate temperature increases, enhance its relevance to the built environment, especially for informal settlements. The significance of prioritizing this BIPVGREEN climate mitigation action in low-income vulnerable regions and informal settlements is crucial through the minimum tax financing worldwide and citizen's engagement in architectural BIPVGREEN co-integration.
IOP Publishing
2024
2024
2024
2024
An extraordinary charge transfer kinetics and chemical stability make a boron-doped diamond (BDD) a promising material for electrochemical applications including wastewater treatment. Yet, with flat geometrical surfaces its scaling options are limited. In this study, the reticulated Vitreous Carbon (RVC) served as a substrate for boron-doped diamondized nanocarbons (BDNC) film growth resulting with complex heterogeneity carbon structures with different morphologies defined by using electron microscopy, microtomography, activation energy studies, and Raman spectroscopy.
The proposed modification significantly boosted the electrochemical Fe(CN)63−/4− redox activity. The voltammetry and impedimetric studies revealed its origin as a significantly higher share of electrochemically active sites at the BDNC@RVC electrode (increased by 114 %) combined with enhanced heterogeneous rate constant (2× increase up to 8.24·10−4 cm s−1). Finally, to establish its applicability for water treatment, the BDNC@RVC was studied as the anode in electrochemical paracetamol decomposition. Boron-enriched nanoarchitecture formed at the RVC electrode surface substantially reduced the oxidation energy barrier manifested as a decrease in activation overpotential by 212 mV, which gave a consequence in a 78 % removal rate (in 4 h, at 0.7 mA cm−2), 12 % higher than bare RVC and yielding lower amounts of APAP decomposition intermediates.
2024
Air quality in Sandefjord, Norway. November 2021 – August 2023.
This report examines the air quality patterns in terms of particulate matter with a diameter less than 2.5 μm (PM2.5) in Sandefjord, Norway. PM2.5 was monitored through five low-cost sensors in hourly resolution from November 2021 to August 2023. The sensors’ reliability is high, with consistent PM2.5 measurements and similar variation over time. Occasional extreme PM2.5 was attributed to local contributions with higher values observed during cold months, or specific long-range transport events. Overall, Sandefjord maintained good air quality for most of the measurement period with daily PM2.5 levels below the air quality criteria. Residential heating activities (wood burning) is the most significant local source, being more pronounced during winter.
NILU
2024
Dechloranes and chlorinated paraffins in sediments and biota of two subarctic lakes
Our understanding of the environmental behavior, bioaccumulation and concentrations of chlorinated paraffins (CPs) and Dechloranes (Dec) in the Arctic environment is still limited, particularly in freshwater ecosystems. In this descriptive study, short chain (SCCPs) and medium chain (MCCPs) CPs, Dechlorane Plus (DP) and analogues, and polychlorinated biphenyls (PCBs) were measured in sediments, benthic organisms, three-spined stickleback (Gasterosteus aculeatus), Arctic char (Salvelinus alpinus) and brown trout (Salmo trutta) in two Sub-Arctic lakes in Northern Norway. Takvannet (TA) is a remote lake, with no known local sources for organic contaminants, while Storvannet (ST) is situated in a populated area. SCCPs and MCCPs were detected in all sediment samples from ST with concentration of 42.26–115.29 ng/g dw and 66.18–136.69 ng/g dw for SCCPs and MCCPs, respectively. Only SCCPs were detected in TA sediments (0.4–5.28 ng/g dw). In biota samples, sticklebacks and benthic organisms showed the highest concentrations of CPs, while concentrations were low or below detection limits in both char and trout. The congener group patterns observed in both lakes showed SCCP profiles dominated by higher chlorinated congener groups while the MCCPs showed consistency in their profiles, with C14 being the most prevalent carbon chain length. Anti- and syn-DP isomers were detected in all sediment, benthic and stickleback samples with higher concentrations in ST than in TA. However, they were only present in a few char and trout samples from ST. Dec 601 and 604 were below detection limits in all samples in both lakes. Dec 603 was detected only in ST sediments, sticklebacks and 2 trout samples, while Dec 602 was the only DP analogue found in all samples from both lakes. While there were clear differences in sediment concentrations of DP and Dec 602 between ST and TA, differences between lakes decreased with increasing δ15N. This pattern was similar to the PCB behavior, suggesting the lake characteristics in ST are playing an important role in the lack of biomagnification of pollutants in this lake. Our results suggest that ST receives pollutants from local sources in addition to atmospheric transport.
Frontiers Media S.A.
2024