scholarly journals A Methodology for Designing Short-Term Stationary Air Quality Campaigns with Mobile Laboratories Using Different Possible Allocation Criteria

2021 ◽  
Vol 13 (13) ◽  
pp. 7481
Author(s):  
Samuele Marinello ◽  
Massimo Andretta ◽  
Patrizia Lucialli ◽  
Elisa Pollini ◽  
Serena Righi
Keyword(s):  
Air Quality ◽  
Atmospheric Dispersion ◽  
Monitoring Networks ◽  
Monitoring And Control ◽  
Short Term ◽  
Atmospheric Dispersion Models ◽  
Emilia Romagna Region ◽  
Key Issues ◽  
Two Phases ◽  
Allocation Algorithms

Air quality monitoring and control are key issues for environmental assessment and management in order to protect public health and the environment. Local and central authorities have developed strategies and tools to manage environmental protection, which, for air quality, consist of monitoring networks with fixed and portable instrumentation and mathematical models. This study develops a methodology for designing short-term air quality campaigns with mobile laboratories (laboratories fully housed within or transported by a vehicle and maintained in a fixed location for a period of time) as a decision support system for environmental management and protection authorities. In particular, the study provides a methodology to identify: (i) the most representative locations to place mobile laboratories and (ii) the best time period to carry out the measurements in the case of short-term air quality campaigns. The approach integrates atmospheric dispersion models and allocation algorithms specifically developed for optimizing the measuring campaigns. The methodology is organized in two phases, each of them divided into several steps. Fourteen allocation algorithms dedicated to three type of receptors (population, vegetation and physical cultural heritage) have been proposed. The methodology has been applied to four short-term air quality campaigns in the Emilia-Romagna region.

scholarly journals Real-time use of inverse modeling techniques to assess the atmospheric accidental release from a nuclear power plant

2020 ◽  
Vol 55 (2) ◽  
pp. 107-115
Author(s):  
O. Saunier ◽  
I. Korsakissok ◽  
D. Didier ◽  
T. Doursout ◽  
A. Mathieu
Keyword(s):  
Inverse Modeling ◽  
Nuclear Power ◽  
Source Term ◽  
Atmospheric Dispersion ◽  
Air Concentration ◽  
Fukushima Accident ◽  
Modeling Tool ◽  
Modeling Techniques ◽  
Atmospheric Dispersion Models ◽  
Key Issues

The assessment of the source term including the time evolution of the release rate into the atmosphere and its distribution between radionuclides is one of the key issues in the understanding of the consequences of a nuclear accident. Inverse modeling methods, which combine environmental measurements, and atmospheric dispersion models have been proven to be efficient in assessing the source term due to an accidental situation. We developed our own tool, which has been applied to the Fukushima accident by using dose rate measurements and air concentration measurements. The inverse modeling tool has been implemented and tested during exercises implying fictitious radioactive releases with the aim of testing this method for emergency management. The exercises showed the relevance of the inverse modeling tool and it is a rewarding experience, which helped us to identify the required developments for the purpose of an operational use.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

scholarly journals Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

2021 ◽  
Vol 14 (1) ◽  
pp. 37-52
Author(s):  
Ravi Sahu ◽  
Ayush Nagal ◽  
Kuldeep Kumar Dixit ◽  
Harshavardhan Unnibhavi ◽  
Srikanth Mantravadi ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Metric Learning ◽  
Quality Monitoring ◽  
Series Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Calibration Algorithm ◽  
Two Phases ◽  
Non Parametric

Abstract. Low-cost sensors offer an attractive solution to the challenge of establishing affordable and dense spatio-temporal air quality monitoring networks with greater mobility and lower maintenance costs. These low-cost sensors offer reasonably consistent measurements but require in-field calibration to improve agreement with regulatory instruments. In this paper, we report the results of a deployment and calibration study on a network of six air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed in two phases over a period of 3 months at sites situated within two megacities with diverse geographical, meteorological and air quality parameters. A unique feature of our deployment is a swap-out experiment wherein three of these sensors were relocated to different sites in the two phases. This gives us a unique opportunity to study the effect of seasonal, as well as geographical, variations on calibration performance. We report an extensive study of more than a dozen parametric and non-parametric calibration algorithms. We propose a novel local non-parametric calibration algorithm based on metric learning that offers, across deployment sites and phases, an R2 coefficient of up to 0.923 with respect to reference values for O3 calibration and up to 0.819 for NO2 calibration. This represents a 4–20 percentage point increase in terms of R2 values offered by classical non-parametric methods. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature into calibration models; (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature-weighing or metric-learning technique; (3) using local calibration techniques such as k nearest neighbors (KNN); (4) performing temporal smoothing over raw time series data but being careful not to do so too aggressively; and (5) making all efforts to ensure that data with enough diversity are demonstrated in the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors and offer an encouraging opportunity to use them to supplement and densify compliance regulatory monitoring networks.

scholarly journals Improving the spatial resolution of air-quality modelling at a European scale – development and evaluation of the Air Quality Re-gridder Model (AQR v1.1)

2016 ◽  
Vol 9 (12) ◽  
pp. 4475-4489 ◽  
Author(s):  
Mark R. Theobald ◽  
David Simpson ◽  
Massimo Vieno
Keyword(s):  
Air Quality ◽  
Spatial Variability ◽  
Spatial Resolution ◽  
Atmospheric Chemistry ◽  
High Spatial Resolution ◽  
Atmospheric Dispersion ◽  
Substantial Improvement ◽  
Monitoring Networks ◽  
Concentration Data ◽  
Emission Data

Abstract. Currently, atmospheric chemistry and transport models (ACTMs) used to assess impacts of air quality, applied at a European scale, lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, the Air Quality Re-gridder (AQR) model has been developed to estimate the spatial distributions (at a spatial resolution of 1  ×  1 km2) of annual mean atmospheric concentrations within the grid squares of an ACTM (in this case with a spatial resolution of 50  ×  50 km2). This is done as a post-processing step by combining the coarse-resolution ACTM concentrations with high-spatial-resolution emission data and simple parameterisations of atmospheric dispersion. The AQR model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that AQR gives a substantial improvement on the predictions of the ACTM, reducing both mean model error (from 61 to 41 % for NO2 and from 42 to 27 % for NH3) and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.39 for NO2 and from 0.74 to 0.84 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high-spatial-resolution emission data, which are not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gave an improvement on the ACTM predictions for the two test domains. The AQR model provides an easy-to-use and robust method to estimate sub-grid variability that can potentially be extended to different timescales and pollutants.

scholarly journals A sub-grid model for improving the spatial resolution of air quality modelling at a European scale

2016 ◽  
Author(s):  
Mark R. Theobald ◽  
David Simpson ◽  
Massimo Vieno
Keyword(s):  
Air Quality ◽  
Spatial Variability ◽  
Spatial Resolution ◽  
Atmospheric Chemistry ◽  
High Spatial Resolution ◽  
Atmospheric Dispersion ◽  
Monitoring Networks ◽  
Concentration Data ◽  
Emission Data ◽  
Grid Model

Abstract. Currently, atmospheric chemistry and transport models (CTMs) used to assess impacts of air quality applied at a European scale lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, a sub-grid model has been developed to estimate the spatial distributions (at a spatial resolution of 1 × 1 km2) of annual mean atmospheric concentrations within the grid squares of a CTM (in this case with a spatial resolution of 50 × 50 km2). This is done by combining high spatial resolution emission data with simple parameterisations of atmospheric dispersion. The sub-grid model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that the sub-grid model represents a substantial improvement on the predictions of the CTM, reducing both mean model error (from 60 % to 40 % for NO2 and from 42 % to 26 % for NH3 and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.42 for NO2 and from 0.74 to 0.85 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high spatial resolution emission data, which is not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gives an improvement on the CTM predictions for the two test domains. The sub-grid model provides a simple and robust method to estimate sub-grid variability that can potentially be extended to different time scales and pollutants.

scholarly journals The casual effects of COVID-19 lockdown on air quality and short-term health impacts in China

2021 ◽  
Vol 290 ◽  
pp. 117988 ◽  
Author(s):  
Li Yumin ◽  
Li Shiyuan ◽  
Huang Ling ◽  
Liu Ziyi ◽  
Zhu Yonghui ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Impacts ◽  
Short Term
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