scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals Dynamic evaluation of a multi-year model simulation of particulate matter concentrations over Europe

2013 ◽  
Vol 13 (8) ◽  
pp. 4319-4337 ◽  
Author(s):  
È. Lecœur ◽  
C. Seigneur
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Transport Model ◽  
Model Simulation ◽  
Model Performance ◽  
Monitoring And Evaluation ◽  
The Other ◽  
Chemical Transport Model ◽  
Performance Goal ◽  
Dynamic Evaluation

Abstract. A 9 yr air quality simulation is conducted from 2000 to 2008 over Europe using the Polyphemus/Polair3D chemical-transport model (CTM) and then evaluated against the measurements of the European Monitoring and Evaluation Programme (EMEP). The spatial distribution of PM2.5 over Europe shows high concentrations over northern Italy (36 μg m−3) and some areas of Eastern Europe, France, and Benelux, and low concentrations over Scandinavia, Spain, and the easternmost part of Europe. PM2.5 composition differs among regions. The operational evaluation shows satisfactory model performance for ozone (O3). PM2.5, PM10, and sulfate (SO4=) meet the performance goal of Boylan and Russell (2006). Nitrate (NO3−) and ammonium (NH4+) are overestimated, although NH4+ meets the performance criterion. The correlation coefficients between simulated and observed data are 63% for O3, 57% for PM10, 59% for PM2.5, 57% for SO4=, 42% for NO3−, and 58% for NH4+. The comparison with other recent 1 yr model simulations shows that all models overestimate nitrate. The performance of PM2.5, sulfate, and ammonium is comparable to that of the other models. The dynamic evaluation shows that the response of PM2.5 to changes in meteorology differs depending on location and the meteorological variable considered. Wind speed and precipitation show a strong negative day-to-day correlation with PM2.5 and its components (except for sea salt, which shows a positive correlation), which tends towards 0 as the day lag increases. On the other hand, the correlation coefficient is near constant for temperature, for any day lag and PM2.5 species, but it may be positive or negative depending on the species and, for sulfate, depending on the location. The effects of precipitation and wind speed on PM2.5 and its components are better reproduced by the model than the effects of temperature. This is mainly due to the fact that temperature has different effects on the PM2.5 components, unlike precipitation and wind speed, which impact most of the PM2.5 components in the same way. These results suggest that state-of-the-science air quality models reproduce satisfactorily the effect of meteorology on PM2.5 and therefore are suitable to investigate the effects of climate change on particulate air quality, although uncertainties remain concerning semivolatile PM2.5 components.

scholarly journals Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1633
Author(s):  
Andrés Yarce Botero ◽  
Santiago Lopez-Restrepo ◽  
Nicolás Pinel Peláez ◽  
Olga L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
South America ◽  
Transport Model ◽  
Monitoring Network ◽  
Temporal Variations ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Model Simulations ◽  
Monitoring Instrument ◽  
Data Assimilation Technique

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NOx top-down emissions using the regional chemical transport model LOTOS-EUROS with the NO2 observations from the TROPOspheric Monitoring Instrument (TROPOMI). The assimilation was performed for a domain in the northwest of South America centered over Colombia, and includes regions in Panama, Venezuela and Ecuador. In the 4DEnVar approach, the implementation of the linearized and adjoint model are avoided by generating an ensemble of model simulations and by using this ensemble to approximate the nonlinear model and observation operator. Emission correction parameters’ locations were defined for positions where the model simulations showed significant discrepancies with the satellite observations. Using the 4DEnVar data assimilation method, optimal emission parameters for the LOTOS-EUROS model were estimated, allowing for corrections in areas where ground observations are unavailable and the region’s emission inventories do not correctly reflect the current emissions activities. The analyzed 4DEnVar concentrations were compared with the ground measurements of one local air quality monitoring network and the data retrieved by the satellite instrument Ozone Monitoring Instrument (OMI). The assimilation had a low impact on NO2 surface concentrations reducing the Mean Fractional Bias from 0.45 to 0.32, primordially enhancing the spatial and temporal variations in the simulated NO2 fields.

scholarly journals Dynamic evaluation of a multi-year model simulation of particulate matter concentrations over Europe

2013 ◽  
Vol 13 (1) ◽  
pp. 475-526
Author(s):  
È. Lecœur ◽  
C. Seigneur
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Transport Model ◽  
Model Simulation ◽  
Model Performance ◽  
The Other ◽  
Performance Criteria ◽  
Chemical Transport Model ◽  
Performance Goal ◽  
Dynamic Evaluation

Abstract. A nine-year air quality simulation is conducted from 2000 to 2008 over Europe using the Polyphemus/Polair3D chemical-transport model (CTM) and then evaluated against the measurements of the European Monitoring and Evaluation Programme (EMEP). The spatial distribution of PM2.5 over Europe shows high concentrations over northern Italy (36 μg m−3) and some areas of eastern Europe, France, and Benelux, and low concentrations over Scandinavia, Spain, and the easternmost part of Europe. PM2.5 composition differs among regions. The operational evaluation shows satisfactory model performance for ozone (O3). PM2.5, PM10, and sulfate (SO42−) meet the performance goal of Boylan and Russell (2006). Nitrate (NO3−) and ammonium (NH4+) are overestimated, although NH4+ meets the performance criteria. The correlation coefficients between simulated and observed data are 63% for O3, 57% for PM10, 59% for PM2.5, 57% for SO42−, 42% for NO3−, and 58% for NH4+. The comparison with other recent one-year model simulations shows that all models overestimate nitrate. The performance of PM2.5, sulfate, and ammonium is comparable to that of the other models. The dynamic evaluation shows that the response of PM2.5 to changes in meteorology differs depending on location and the meteorological variable considered. Wind speed and precipitation show a strong negative day-to-day correlation with PM2.5 and its components (except for sea salt, which shows a positive correlation), that tends towards 0 as the day lag increases. On the other hand, the correlation coefficient is near constant for temperature, for any day lag and PM2.5 species, but it may be positive or negative depending on the species and, for sulfate, depending on the location. The effects of precipitation and wind speed on PM2.5 and its components are better reproduced by the model than the effects of temperature. This is mainly due to the fact that temperature has different effects on the PM2.5 components, unlike precipitation and wind speed which impact most of the PM2.5 components in the same way. These results suggest that state-of-the-science air quality models reproduce satisfactorily the effect of meteorology on PM2.5 and, therefore, are suitable to investigate the effects of climate change on particulate air quality.

scholarly journals Using machine learning to estimate wildfire PM2.5 at California ZIP codes (2006-2020)

2021 ◽  
Author(s):  
Rosana Aguilera ◽  
Nana Luo ◽  
Rupa Basu ◽  
Jun Wu ◽  
Alexander Gershunov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Transport Model ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Machine Learning Algorithms ◽  
Large Set ◽  
Chemical Transport Model ◽  
Computationally Intensive ◽  
The U.S

Epidemiological studies on the detrimental health impacts of exposure to fine particulate matter (PM2.5) from different sources of emission can inform regulatory policy and identify vulnerable communities. Though PM2.5 has decreased in the U.S. in the two past decades, the increasing frequency and severity of wildfires contribute to episodically impair air quality in wildfire-prone regions and beyond. Monitoring air quality extensively is challenging. Since government-operated monitors are sparsely located across California and the U.S., several regions and populations remain unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on gathering large amounts of data, such as satellite-derived aerosol properties and meteorological variables. and direct use of low-cost air sensor measurements that may be associated with substantial uncertainty Furthermore, modelling wildfire-specific PM2.5 is often based on chemical transport model predictions, which results in highly computationally intensive efforts. Our study used an ensemble model that integrated multiple machine learning algorithms and a large set of predictor variables to estimate daily PM2.5 at the ZIP code level, a relevant spatio-temporal resolution for epidemiological and public health studies. Our models achieved comparable results to previous machine learning studies for PM2.5 prediction, but avoided processing larger, computationally intensive datasets. In addition, we use machine learning to estimate the wildfire-specific PM2.5 concentrations through a novel multiple imputation approach.

scholarly journals Assessing Douro Vineyards Exposure to Tropospheric Ozone

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 200
Author(s):  
Ana Ascenso ◽  
Carla Gama ◽  
Daniel Blanco-Ward ◽  
Alexandra Monteiro ◽  
Carlos Silveira ◽  
...  
Keyword(s):  
Air Quality ◽  
Tropospheric Ozone ◽  
Transport Model ◽  
Sugar Content ◽  
Productivity Loss ◽  
Ozone Exposure ◽  
Vitis Vinifera L ◽  
Chemical Transport Model ◽  
Wine Production ◽  
Exposure Response

Tropospheric ozone (O3) can strongly damage vegetation. Grapevines (Vitis vinifera L.), in particular, have intermediate sensitivity to ozone. Wine production is an important economic activity, as well as a pillar to the cultural identity of several countries in the world. This study aims to evaluate the risk of Douro vineyards exposure to ozone, by estimating its concentration and deposition in the Demarcated Region of Douro in Portugal. Based on an assessment of the climatology of the area, the years 2003 to 2005 were selected among the hottest years of the recent past, and the chemical transport model CHIMERE was used to estimate the three-dimensional field of ozone and its dry deposition over the Douro region with 1 km2 of horizontal resolution. Model results were validated by comparison with measured data from the European air quality database (AirBase). The exposure indicator AOT40 (accumulated concentration of ozone above 40 ppb) was calculated and an exposure–response function was applied to determine the grapevine risk to ozone exposure. The target value for the protection of vegetation established by the Air Quality Framework Directive was exceeded on most of the Douro region, especially over the Baixo Corgo and Cima Corgo sub-regions. The results of the exposure–response functions suggest that the productivity loss can reach 27% and that the sugar content of the grapes could be reduced by 32%, but these values are affected by the inherent uncertainty of the used methodology.

scholarly journals A new diagnostic for tropospheric ozone production

2017 ◽  
Author(s):  
Peter M. Edwards ◽  
Mathew J. Evans
Keyword(s):  
Air Quality ◽  
Tropospheric Ozone ◽  
Transport Model ◽  
Chemical Transport ◽  
Ozone Production ◽  
Chemical Transport Model ◽  
Organic Species ◽  
Earth’S Climate ◽  
Oxidation Of Organics

Abstract. Tropospheric ozone is important for the Earth’s climate and air quality. It is produced during the oxidation of organics in the presence of nitrogen oxides. Due to the range of organic species emitted and the chain like nature of their oxidation, this chemistry is complex and understanding the role of different processes (emission, deposition, chemistry) is difficult. We demonstrate a new methodology for diagnosing ozone production based on the processing of bonds contained within emitted molecules, the fate of which is determined by the conservation of spin of the bonding electrons. Using this methodology to diagnose ozone production in the GEOS-Chem chemical transport model, we demonstrate its advantages over the standard diagnostic. We show that the number of bonds emitted, their chemistry and lifetime, and feedbacks on OH are all important in determining the ozone production within the model and its sensitivity to changes. This insight may allow future model-model comparisons to better identify the root causes of model differences.

scholarly journals The added value of a visible channel to a geostationary thermal infrared instrument to monitor ozone for air quality

2014 ◽  
Vol 7 (2) ◽  
pp. 1645-1689
Author(s):  
E. Hache ◽  
J.-L. Attié ◽  
C. Tourneur ◽  
P. Ricaud ◽  
L. Coret ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Statistical Tests ◽  
Surface Ozone ◽  
A Priori ◽  
Thermal Infrared ◽  
Added Value ◽  
Chemical Transport Model ◽  
Geo Satellite ◽  
Sky Conditions

Abstract. Ozone is a tropospheric pollutant and plays a key role in determining the air quality that affects human wellbeing. In this study, we compare the capability of two hypothetical grating spectrometers onboard a geostationary (GEO) satellite to sense ozone in the lowermost troposphere (surface and the 0–1 km column). We consider one week during the Northern Hemisphere summer simulated by a chemical transport model, and use the two GEO instrument configurations to measure ozone concentration (1) in the thermal infrared (GEO TIR) and (2) in the thermal infrared and the visible (GEO TIR+VIS). These configurations are compared against each other, and also against an ozone reference state and a priori ozone information. In a first approximation, we assume clear sky conditions neglecting the influence of aerosols and clouds. A number of statistical tests are used to assess the performance of the two GEO configurations. We consider land and sea pixels and whether differences between the two in the performance are significant. Results show that the GEO TIR+VIS configuration provides a better representation of the ozone field both for surface ozone and the 0–1 km ozone column during the daytime especially over land.

scholarly journals Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

2018 ◽  
Author(s):  
Scott D. Chambers ◽  
Elise-Andree Guérette ◽  
Khalia Monk ◽  
Alan D. Griffiths ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Transport Models ◽  
Potential Health ◽  
Meteorological Model ◽  
Model Skill ◽  
Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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