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Chen HW, Chen CY, Lin GY. Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:16048-16065. [PMID: 38308783 DOI: 10.1007/s11356-024-32226-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024]
Abstract
Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.
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Affiliation(s)
- Ho-Wen Chen
- Department of Environmental Science and Engineering, Tung-Hai University, Taichung, Taiwan
| | - Chien-Yuan Chen
- Department of Civil and Water Resources Engineering, National Chiayi University, Chiayi, Taiwan
| | - Guan-Yu Lin
- Department of Environmental Science and Engineering, Tung-Hai University, Taichung, Taiwan.
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Bulot FM, Ossont SJ, Morris AK, Basford PJ, Easton NH, Mitchell HL, Foster GL, Cox SJ, Loxham M. Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance. Heliyon 2023; 9:e15943. [PMID: 37187904 PMCID: PMC10176080 DOI: 10.1016/j.heliyon.2023.e15943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/03/2023] [Accepted: 04/27/2023] [Indexed: 05/17/2023] Open
Abstract
Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM2.5 background concentration of 5.5 μg/m3, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.
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Affiliation(s)
- Florentin M.J. Bulot
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- Corresponding author. University of Southampton, Southampton, UK.
| | | | | | - Philip J. Basford
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Natasha H.C. Easton
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- National Oceanography Centre, Southampton, UK
- Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, UK
| | - Hazel L. Mitchell
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Gavin L. Foster
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- School of Ocean and Earth Science, National Oceanography Centre, University of Southampton, UK
| | - Simon J. Cox
- Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Matthew Loxham
- Southampton Marine and Maritime Institute, University of Southampton, Southampton, UK
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health Research Southampton Biomedical Research Centre, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
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Ciampittiello M, Manca D, Dresti C, Grisoni S, Lami A, Saidi H. Meteo-Hydrological Sensors within the Lake Maggiore Catchment: System Establishment, Functioning and Data Validation. SENSORS 2021; 21:s21248300. [PMID: 34960394 PMCID: PMC8705426 DOI: 10.3390/s21248300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/29/2021] [Accepted: 12/07/2021] [Indexed: 12/01/2022]
Abstract
Climate change and human activities have a strong impact on lakes and their catchments, so to understand ongoing processes it is fundamental to monitor environmental variables with a spatially well-distributed and high frequency network and efficiently share data. An effective sharing and interoperability of environmental information between technician and end-user fosters an in-depth knowledge of the territory and its critical environmental issues. In this paper, we present the approaches and the results obtained during the PITAGORA project (Interoperable Technological Platform for Acquisition, Management and Organization of Environmental data, related to the lake basin). PITAGORA was aimed at developing both instruments and data management, including pre-processing and quality control of raw data to ensure that data are findable, accessible, interoperable, and reusable (FAIR principles). The main results show that the developed instrumentation is low-cost, easily implementable and reliable, and can be applied to the measurement of diverse environmental parameters such as meteorological, hydrological, physico-chemical, and geological. The flexibility of the solutions proposed make our system adaptable to different monitoring purposes, research, management, and civil protection. The real time access to environmental information can improve management of a territory and ecosystems, safety of the population, and sustainable socio-economic development.
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Carotenuto F, Brilli L, Gioli B, Gualtieri G, Vagnoli C, Mazzola M, Viola AP, Vitale V, Severi M, Traversi R, Zaldei A. Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1919. [PMID: 32235527 PMCID: PMC7180591 DOI: 10.3390/s20071919] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 11/17/2022]
Abstract
The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m-3 for PM2.5 and ≈3 µg m-3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m-3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.
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Affiliation(s)
- Federico Carotenuto
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
| | - Lorenzo Brilli
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
| | - Beniamino Gioli
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
| | - Giovanni Gualtieri
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
| | - Carolina Vagnoli
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
| | - Mauro Mazzola
- Institute of Polar Sciences, National Research Council of Italy (CNR ISP), 40129 Bologna (BO), Italy; (M.M.); (A.P.V.); (V.V.)
| | - Angelo Pietro Viola
- Institute of Polar Sciences, National Research Council of Italy (CNR ISP), 40129 Bologna (BO), Italy; (M.M.); (A.P.V.); (V.V.)
| | - Vito Vitale
- Institute of Polar Sciences, National Research Council of Italy (CNR ISP), 40129 Bologna (BO), Italy; (M.M.); (A.P.V.); (V.V.)
| | - Mirko Severi
- Chemistry Department, University of Florence, 50019 Sesto Fiorentino (FI), Italy; (M.S.); (R.T.)
| | - Rita Traversi
- Chemistry Department, University of Florence, 50019 Sesto Fiorentino (FI), Italy; (M.S.); (R.T.)
| | - Alessandro Zaldei
- Institute of BioEconomy, National Research Council of Italy (CNR IBE), 50019 Sesto Fiorentino (FI), Italy; (L.B.); (G.G.); (C.V.); (A.Z.)
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