scholarly journals Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability

2017 ◽  
Author(s):  
Benjamin Brown-Steiner ◽  
Noelle E. Selin ◽  
Ronald G. Prinn ◽  
Erwan Monier ◽  
Simone Tilmes ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Signal Detection ◽  
Surface Ozone ◽  
Quality Data ◽  
Spatial Averaging ◽  
Ozone Data ◽  
Detection Capabilities ◽  
The Mean ◽  
Temporal And Spatial

Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical and meteorological variabilities that exist both in space and in time. This can present difficulties for both policy-makers and researchers as they attempt to identify the influence or signal of climate trends (e.g. any pauses in warming trends), the impact of enacted emission reductions policies (e.g. United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g. summertime ozone over the Northeastern United States). Here we examine the scale-dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the Continental US. We recommend temporal averaging of at least 10–15 years combined with regional spatial averaging over several hundred kilometer spatial scales. These results are consistent between simulated and observed data, and within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data, and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

scholarly journals Maximizing ozone signals among chemical, meteorological, and climatological variability

2018 ◽  
Vol 18 (11) ◽  
pp. 8373-8388 ◽  
Author(s):  
Benjamin Brown-Steiner ◽  
Noelle E. Selin ◽  
Ronald G. Prinn ◽  
Erwan Monier ◽  
Simone Tilmes ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Signal Detection ◽  
Surface Ozone ◽  
Quality Data ◽  
Spatial Averaging ◽  
Ozone Data ◽  
Detection Capabilities ◽  
The Mean ◽  
Temporal And Spatial

Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

scholarly journals Impact of lightning-NO on eastern United States photochemistry during the summer of 2006 as determined using the CMAQ model

2012 ◽  
Vol 12 (4) ◽  
pp. 1737-1758 ◽  
Author(s):  
D. J. Allen ◽  
K. E. Pickering ◽  
R. W. Pinder ◽  
B. H. Henderson ◽  
K. W. Appel ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Wet Deposition ◽  
Surface Ozone ◽  
The United States ◽  
South Central ◽  
Southeastern Us ◽  
The Mean ◽  
The Impact ◽  
No Emissions

Abstract. A lightning-nitrogen oxide (NO) algorithm is implemented in the Community Multiscale Air Quality Model (CMAQ) and used to evaluate the impact of lightning-NO emissions (LNOx) on tropospheric photochemistry over the United States during the summer of 2006. For a 500 mole per flash lightning-NO source, the mean summertime tropospheric NO2 column agrees with satellite-retrieved columns to within −5 to +13%. Temporal fluctuations in the column are moderately well simulated; however, the addition of LNOx does not lead to a better simulation of day-to-day variability. The contribution of lightning-NO to the model column ranges from ∼10% in the northern US to >45% in the south-central and southeastern US. Lightning-NO adds up to 20 ppbv to upper tropospheric model ozone and 1.5–4.5 ppbv to 8-h maximum surface layer ozone, although, on average, the contribution of LNOx to model surface ozone is 1–2 ppbv less on poor air quality days. LNOx increases wet deposition of oxidized nitrogen by 43% and total deposition of nitrogen by 10%. This additional deposition reduces the mean magnitude of the CMAQ low-bias in nitrate wet deposition with respect to National Atmospheric Deposition monitors to near zero. Differences in urban/rural biases between model and satellite-retrieved NO2 columns were examined to identify possible problems in model chemistry and/or transport. CMAQ columns were too large over urban areas. Biases at other locations were minor after accounting for the impacts of lightning-NO emissions and the averaging kernel on model columns. In order to obtain an upper bound on the contribution of uncertainties in NOy chemistry to upper tropospheric NOx low biases, sensitivity calculations with updated chemistry were run for the time period of the Intercontinental Chemical Transport Experiment (INTEX-A) field campaign (summer 2004). After adjusting for possible interferences in NO2 measurements and averaging over the entire campaign, these updates reduced 7–9 km biases from 32 to 17% and 9–12 km biases from 57 to 46%. While these changes lead to better agreement, a considerable unexplained NO2 low-bias remains in the uppermost troposphere.

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

scholarly journals Impact of the 2008 Global Recession on air quality over the United States: Implications for surface ozone levels from changes in NOxemissions

2016 ◽  
Vol 43 (17) ◽  
pp. 9280-9288 ◽  
Author(s):  
Daniel Tong ◽  
Li Pan ◽  
Weiwei Chen ◽  
Lok Lamsal ◽  
Pius Lee ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Surface Ozone ◽  
The United States ◽  
Global Recession ◽  
Ozone Levels

scholarly journals Impact of lightning-NO on Eastern United States photochemistry during the summer of 2006 as determined using the CMAQ model

2011 ◽  
Vol 11 (6) ◽  
pp. 17699-17757 ◽  
Author(s):  
D. J. Allen ◽  
K. E. Pickering ◽  
R. W. Pinder ◽  
B. H. Henderson ◽  
K. W. Appel ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Wet Deposition ◽  
Surface Ozone ◽  
Convective Precipitation ◽  
Eastern United States ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
The Impact

Abstract. A lightning-nitrogen oxide (NO) algorithm is developed for the regional Community Multiscale Air Quality Model (CMAQ) and used to evaluate the impact of lightning-NO emissions (LNOx) on tropospheric photochemistry over the Eastern United States during the summer of 2006. The scheme assumes flash rates are proportional to the model convective precipitation rate but then adjusts the flash rates locally to match monthly average observations. Over the Eastern United States, LNOx is responsible for 20–25 % of the tropospheric nitrogen dioxide (NO2) column. This additional NO2 reduces the low-bias of simulated NO2 columns with respect to satellite-retrieved Dutch Ozone Monitoring Instrument NO2 (DOMINO) columns from 41 to 14 %. It also adds 10–20 ppbv to upper tropospheric ozone and 1.5–4.5 ppbv to 8-h maximum surface layer ozone, although, on average, the contribution of LNOx to surface ozone is 1–2 ppbv less on poor air quality days. Biases between modeled and satellite-retrieved tropospheric NO2 columns vary greatly between urban and rural locations. In general, CMAQ overestimates columns at urban locations and underestimates columns at rural locations. These biases are consistent with in situ measurements that also indicate that CMAQ has too much NO2 in urban regions and not enough in rural regions. However, closer analysis suggests that most of the differences between modeled and satellite-retrieved urban to rural ratios are likely a consequence of the horizontal and vertical smoothing inherent in columns retrieved by the Ozone Monitoring Instrument (OMI). Within CMAQ, LNOx increases wet deposition of nitrate by 50 % and total deposition of nitrogen by 11 %. This additional deposition reduces the magnitude of the CMAQ low-bias in nitrate wet deposition with respect to National Atmospheric Deposition monitors to near zero. In order to obtain an upper bound on the contribution of uncertainties in chemistry to upper tropospheric NOx low biases, sensitivity calculations with updated chemistry were run for the time period of the Intercontinental Chemical Transport Experiment (INTEX-A) field campaign (summer 2004). After adjusting for possible interferences in NO2 measurements and averaging over the entire campaign, these updates reduced 7–9 km biases from 32 to 17 % and 9–12 km biases from 57 to 46 %. While these changes lead to better agreement, a considerable NO2 low-bias remains in the uppermost troposphere.

Respiratory Symptoms and Spirometric Observations in Relation to Atmospheric Pollutants in a Sample of Urban Population

2000 ◽  
Vol 12 (2) ◽  
pp. 58-64 ◽  
Author(s):  
K. Satish Kumar ◽  
C.E. Prasad ◽  
N. Balakrishna ◽  
K. Visweswara Rao ◽  
P. Uma Maheswara Reddy
Keyword(s):  
Air Quality ◽  
Respiratory Symptoms ◽  
Ambient Air ◽  
Air Pollutant ◽  
Control Measures ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Group I ◽  
The Mean

The prevalence of respiratory problems and the ventilatory functions in subjects belonging to three sample areas with different levels of pollution was studied to ascertain if there is any association between air pollutant levels and abnormal ventilatory functions. The predominant activity existing in that area served as the basis for stratification of the city into industrial (Group I), commercial (Group II) and residential (Group III) areas. Ambient air quality data of suspended particulate matter SPM, SO2 and NOx of the three sample areas were measured using standard methods. 216 men included in the study were administered the American Thoracic Society - Division of Lung Diseases ATS-DLD respiratory questionnaire, clinically examined and subjected to routine laboratory investigations. Spirometry and salbutamol reversibility tests were performed as per the ATS guidelines 1991. The mean and peak levels of SPM in the commercial area and the peak levels in the residential area were higher than the National Ambient Air Quality Standards (NAAQS). The mean and peak levels of NOx and SO2 in all the three areas were lower than the NAAQS. A high prevalence of ∼ 30-50% of respiratory symptoms was reported in the present study. Respiratory and ventilatory abnormalities were higher in the commercial areas, which are associated with the higher mean and peak levels of SO 2 and the peak levels of NOx. The pollution control measures should also aim at the peak levels of pollutants as they have been shown to exacerbate the respiratory symptoms in the present study. Asia Pac J Public Health 2000;12(2): 58-64

A quasi-operational air quality forecasting system for the contiguous United States (CONUS) 

2021 ◽  
Author(s):  
Rajesh Kumar ◽  
Gabriele Pfister ◽  
Piyush Bhardwaj
Keyword(s):  
United States ◽  
Air Quality ◽  
Information Dissemination ◽  
Surface Ozone ◽  
High Surface ◽  
Model Errors ◽  
Grid Spacing ◽  
Air Quality Forecast ◽  
Forecasting System ◽  
Air Quality Forecasting

<p>We present a research system for regional air quality forecasting over  the contiguous United States (CONUS). This system has been developed at the National Center for Atmospheric Research (NCAR) to support community model development, allow early identification of model errors and biases, and support the atmospheric science community in their research. At the same time, it assists field campaign planning and air quality decision-making. The forecasts aim to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA) and not to replace them. A publicly available information dissemination system has been established that displays various air quality products including a near-real-time evaluation of the model forecasts. Our forecasting system has been producing a 48-h forecast every day at 12 km x 12 km grid spacing over the entire CONUS since June 2019 and at 4 km x 4 km grid spacing in Colorado since June 2020. Here, we will report on the performance of our air quality forecasting system in simulating meteorology, PM2.5, ozone, and NOx for the period of 1 June 2019 to 31 December 2020. Our system showed excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but showed, in parts, relatively larger errors in wind speed. The model captured the seasonal cycle of surface PM2.5 and ozone very well in different regions of CONUS and at different types of sites (urban, suburban, and rural) but generally overestimates summertime surface ozone and fails to capture very high surface PM2.5 events. These shortcomings are being addressed in current work. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at https://www.acom.ucar.edu/firex-aq/forecast.shtml and we invite the community to use our forecasting products for their research, as input for urban scale (< 4 km) air quality forecasts, or the co-development of customized products just to name a few applications.</p>

scholarly journals Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005–2014

2019 ◽  
Vol 16 (18) ◽  
pp. 3314
Author(s):  
Niru Senthilkumar ◽  
Mark Gilfether ◽  
Francesca Metcalf ◽  
Armistead G. Russell ◽  
James A. Mulholland ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Spatial Variation ◽  
Temporal Variation ◽  
Observational Data ◽  
Transport Model ◽  
Chemical Transport ◽  
Daily Basis ◽  
Quality Data ◽  
Chemical Transport Model

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3−, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.

scholarly journals Comparison of TROPOMI/Sentinel-5 Precursor NO<sub>2</sub> observations with ground-based measurements in Helsinki

2020 ◽  
Vol 13 (1) ◽  
pp. 205-218 ◽  
Author(s):  
Iolanda Ialongo ◽  
Henrik Virta ◽  
Henk Eskes ◽  
Jari Hovila ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
A Priori ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Chemical Transport Model ◽  
Early Evaluation ◽  
The Mean ◽  
Urban Sites

Abstract. We present a comparison between satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products and ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer between April and September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10 % and 0.12×1015 molec. cm−2 respectively. The dispersion of these differences (estimated as their standard deviation) is 2.2×1015 molec. cm−2. We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5×7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates (within the retrieval uncertainties) relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement. When only the low values of NO2 total columns or the whole dataset are taken into account, the mean bias slightly increases. The change in bias remains mostly within the uncertainties. We also analyse the consistency between satellite-based data and in situ NO2 surface concentrations measured at the Helsinki–Kumpula air quality station (located a few metres from the Pandora spectrometer). We find similar day-to-day variability between TROPOMI, Pandora and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites). The TROPOMI NO2 maps reveal also spatial features, such as the main traffic ways and the airport area, as well as the effect of the prevailing south-west wind patterns. This is one of the first works in which TROPOMI NO2 retrievals are validated against ground-based observations and the results provide an early evaluation of their applicability for monitoring pollution levels in urban sites. Overall, TROPOMI retrievals are valuable to complement the ground-based air quality data (available with high temporal resolution) for describing the spatio-temporal variability of NO2, even in a relatively small city like Helsinki.

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