scholarly journals Gridded global surface ozone metrics for atmospheric chemistry model evaluation

2016 ◽  
Vol 8 (1) ◽  
pp. 41-59 ◽  
Author(s):  
E. D. Sofen ◽  
D. Bowdalo ◽  
M. J. Evans ◽  
F. Apadula ◽  
P. Bonasoni ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Quality Monitoring ◽  
Ozone Exposure ◽  
Daily Maximum ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Chemistry Research

Abstract. The concentration of ozone at the Earth's surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research. We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent data set for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total data set of approximately 6600 sites and 500 million hourly observations from 1971–2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regionally representative locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This data set is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily 8-hour average (MDA8), sum of means over 35 ppb (daily maximum 8-h; SOMO35), accumulated ozone exposure above a threshold of 40 ppbv (AOT40), and metrics related to air quality regulatory thresholds. Gridded data sets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi:10.5285/08fbe63d-fa6d-4a7a-b952-5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner.

scholarly journals Gridded global surface ozone metrics for atmospheric chemistry model evaluation

2015 ◽  
Vol 8 (2) ◽  
pp. 603-647 ◽  
Author(s):  
E. D. Sofen ◽  
D. Bowdalo ◽  
M. J. Evans ◽  
F. Apadula ◽  
P. Bonasoni ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Climate Model ◽  
Surface Ozone ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Chemistry Research ◽  
Consistent Manner

Abstract. The concentration of ozone at the Earth's surface is measured at many locations across the globe for the purposes of air quality monitoring and atmospheric chemistry research. We have brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent dataset for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative and Aer-Chem-MIP. From a total dataset of approximately 6600 sites and 500 million hourly observations from 1971–2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regional background locations that are appropriate for use in global model evaluation. There is generally good data volume since the start of air quality monitoring networks in 1990 through 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. This dataset is made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. Metrics include the moments of the distribution, percentiles, maximum daily eight-hour average (MDA8), SOMO35, AOT40, and metrics related to air quality regulatory thresholds. Gridded datasets are stored as netCDF-4 files and are available to download from the British Atmospheric Data Centre (doi:10.5285/08fbe63d-fa6d-4a7a-b952-5932e3ab0452). We provide recommendations to the ozone measurement community regarding improving metadata reporting to simplify ongoing and future efforts in working with ozone data from disparate networks in a consistent manner.

scholarly journals On the Use of Air Quality Monitoring Networks for the Evaluation of Nitrogen Oxide Emission Inventories

2008 ◽  
Vol 2 (1) ◽  
pp. 232-248 ◽  
Author(s):  
I.B. Konovalov ◽  
M. Beekmann
Keyword(s):  
Air Quality ◽  
Nitrogen Oxide ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Emission Inventories ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Rural Background ◽  
Study Results ◽  
Different Types

The usefulness of ground based air quality monitoring data for diagnostics of uncertainties in gridded emission inventories is examined. A general probabilistic procedure for comparison of levels of uncertainties in different emission datasets is developed. It implies the evaluation of the agreement between modeling results obtained with these emission datasets and corresponding measurements. This procedure is applied to the evaluation of different datasets for European gridded nitrogen oxide (NOx) emissions by using the AirBase monitoring data and the CHIMERE chemistry-transport model. Numerical experiments are performed for two different types of spatial distributions of emission uncertainties and five different types of monitors. The results are also generalized for various levels of uncertainties in simulated and measured data. It is found, in particular, that most informative, from the point of view of diagnostics of NOx emission uncertainties, are the measurements of NO2 at rural background sites and measurements of ozone at suburban sites situated in the vicinity of intensive sources of emissions. A more precise conclusion regarding the relative accuracy of two emission datasets can be drawn with a larger number of monitors in a network and a higher accuracy of the model and measurements. For example, with a network of 50 rural background NO2 monitors, the probability of choosing the more certain emission data set is more than 90 percent, if differences in uncertainty of two sets are more than 50 percent. Practical recommendations for designing or evolving surface measurement networks, in light of the study results, are given.

scholarly journals Error apportionment for atmospheric chemistry-transport models: a new approach to model evaluation

2016 ◽  
Author(s):  
E. Solazzo ◽  
S. Galmarini
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Model Complexity ◽  
Air Quality Model ◽  
Quality Model ◽  
Causes Of Errors ◽  
Bias Variance

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance, and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved process based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity. The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America. The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the process that caused the error.

Time Series Analysis for Prediction of PM2.5 Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model on Taiwan Air Quality Monitoring Network Data

2020 ◽  
Vol 17 (9) ◽  
pp. 3964-3969
Author(s):  
Doreswamy ◽  
K. S. Harish Kumar ◽  
Ibrahim Gad
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Moving Average ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average

Nowadays, in Taiwan, due to the increasing number of vehicles, industrialization of large energy consumption, uncontrolled constructions and urbanization, air pollution is becoming a major problem. Hence, it is necessary to control air pollution by applying air quality monitoring actions. The particulate matter (PM2.5) of the air pollution in TAQMN data is the main pollutant accountable for at least two-thirds of the severely polluted days in the major cities of Taiwan. In this work, machine learning (ML) techniques are widely used in developing models that can be used to control the air pollution. Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to predict the air pollution concentration, where the dataset chronologically from 2012 to 2016 are used to train the proposed method and testing data set from 2016 to 2017. The result of the SARIMA model shows high precision in forecasting the future values of particulate matter (P2.5) level with minimum error.

scholarly journals Interference from alkenes in chemiluminescent NO<sub><i>x</i></sub> measurements

2020 ◽  
Vol 13 (11) ◽  
pp. 5977-5991
Author(s):  
Mohammed S. Alam ◽  
Leigh R. Crilley ◽  
James D. Lee ◽  
Louisa J. Kramer ◽  
Christian Pfrang ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Systematic Investigation ◽  
Atmospheric Pollutants ◽  
Quality Monitoring ◽  
Ozone Production ◽  
Atmospheric Composition ◽  
Anthropogenic Sources ◽  
Indoor Environments ◽  
Air Quality Monitoring

Abstract. Nitrogen oxides (NOx=NO+NO2) are critical intermediates in atmospheric chemistry and air pollution. NOx levels control the cycling and hence abundance of the primary atmospheric oxidants OH and NO3 and regulate the ozone production which results from the degradation of volatile organic compounds (VOCs) in the presence of sunlight. They are also atmospheric pollutants, and NO2 is commonly included in air quality objectives and regulations. NOx levels also affect the production of the nitrate component of secondary aerosol particles and other pollutants, such as the lachrymator peroxyacetyl nitrate (PAN). The accurate measurement of NO and NO2 is therefore crucial for air quality monitoring and understanding atmospheric composition. The most commonly used approach for the measurement of NO is the chemiluminescent detection of electronically excited NO2 (NO2∗) formed from the NO + O3 reaction within the NOx analyser. Alkenes, ubiquitous in the atmosphere from biogenic and anthropogenic sources, also react with ozone to produce chemiluminescence and thus may contribute to the measured NOx signal. Their ozonolysis reaction may also be sufficiently rapid that their abundance in conventional instrument background cycles, which also utilises the reaction with ozone, differs from that in the measurement cycle such that the background subtraction is incomplete, and an interference effect results. This interference has been noted previously, and indeed, the effect has been used to measure both alkenes and ozone in the atmosphere. Here we report the results of a systematic investigation of the response of a selection of commercial NOx monitors to a series of alkenes. These NOx monitors range from systems used for routine air quality monitoring to atmospheric research instrumentation. The species-investigated range was from short-chain alkenes, such as ethene, to the biogenic monoterpenes. Experiments were performed in the European PHOtoREactor (EUPHORE) to ensure common calibration and samples for the monitors and to unequivocally confirm the alkene levels present (via Fourier transform infrared spectroscopy – FTIR). The instrument interference responses ranged from negligible levels up to 11 %, depending upon the alkene present and conditions used (e.g. the presence of co-reactants and differing humidity). Such interferences may be of substantial importance for the interpretation of ambient NOx data, particularly for high VOC, low NOx environments such as forests or indoor environments where alkene abundance from personal care and cleaning products may be significant.

scholarly journals Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation

2016 ◽  
Vol 16 (10) ◽  
pp. 6263-6283 ◽  
Author(s):  
Efisio Solazzo ◽  
Stefano Galmarini
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Air Quality Model ◽  
Quality Model ◽  
Evaluation Bias ◽  
Causes Of Errors ◽  
Bias Variance

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity.The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the processes that caused the error.

scholarly journals Interference from alkenes in chemiluminescent NO<sub><i>x</i></sub> measurements

2020 ◽  
Author(s):  
Mohammed S. Alam ◽  
Leigh R. Crilley ◽  
James D. Lee ◽  
Louisa J. Kramer ◽  
Christian Pfrang ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Systematic Investigation ◽  
Atmospheric Pollutants ◽  
Quality Monitoring ◽  
Ozone Production ◽  
Atmospheric Composition ◽  
Anthropogenic Sources ◽  
Indoor Environments ◽  
Air Quality Monitoring

Abstract. Nitrogen oxides (NOx = NO + NO2) are critical intermediates in atmospheric chemistry. NOx levels control the cycling and hence abundance of the primary atmospheric oxidants OH and NO3, and regulate the ozone production which results from the degradation of volatile organic compounds (VOCs) in the presence of sunlight. They are also atmospheric pollutants, and NO2 is commonly included in air quality objectives and regulations. NOx levels also affect the production of the nitrate component of secondary aerosol particles and other pollutants such as the lachrymator peroxyacetyl nitrate (PAN). The accurate measurement of NO and NO2 is therefore crucial to air quality monitoring and understanding atmospheric composition. The most commonly used approach for measurement of NO is chemiluminescent detection of electronically excited NO2 (NO2*) from the NO + O3 reaction. Alkenes, ubiquitous in the atmosphere from biogenic and anthropogenic sources, also react with ozone to produce chemiluminescence and thus may contribute to the measured NOx signal. Their ozonolysis reaction may also be sufficiently rapid that their abundance in the instrument background cycle, which also utilises reaction with ozone, differs from the measurement cycle – such that the background subtraction is incomplete, and an interference effect results. This interference has been noted previously, and indeed the effect has been used to measure both alkenes and ozone in the atmosphere. Here we report the results of a systematic investigation of the response of a selection of commercial NOx monitors, ranging from systems used for routine air quality monitoring to atmospheric research instrumentation, to a series of alkenes. Alkenes investigated range from short chain alkenes, such as ethene, to the biogenic monoterpenes. Experiments were performed in the European Photoreactor (EUPHORE) to ensure common calibration and samples for the monitors, and to unequivocally confirm the alkene levels present (via FTIR). The instrument interference responses ranged from negligible levels up to 11 % depending upon the alkene present and conditions used (e.g. presence of co-reactants and differing humidity). Such interferences may be of substantial importance for the interpretation of ambient NOx data, particularly for high-VOC, low-NOx environments such as forests, or indoor environments where alkene abundance from personal care and cleaning products may be significant.

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