scholarly journals Multi-year objective analyses of warm season ground-level ozone and PM<sub>2.5</sub> over North America using real-time observations and Canadian operational air quality models

2013 ◽  
Vol 13 (5) ◽  
pp. 13967-14035 ◽  
Author(s):  
A. Robichaud ◽  
R. Ménard
Keyword(s):  
Air Quality ◽  
North America ◽  
Warm Season ◽  
Ground Level ◽  
Objective Analysis ◽  
Error Statistics ◽  
Ground Level Ozone ◽  
Air Quality Forecast ◽  
Background Check

Abstract. We present multi-year objective analyses (OA) on a high spatio-temporal resolution (15 or 21 km, every hour) for the warm season period (1 May–31 October) for ground-level ozone (2002–2012) and for fine particulate matter (diameter less than 2.5 microns (PM2.5)) (2004–2012). The OA used here combines the Canadian Air Quality forecast suite with US and Canadian surface air quality monitoring sites. The analysis is based on an optimal interpolation with capabilities for adaptive error statistics for ozone and PM2.5 and an explicit bias correction scheme for the PM2.5 analyses. The estimation of error statistics has been computed using a modified version of the Hollingsworth–Lönnberg's (H–L) method. Various quality controls (gross error check, sudden jump test and background check) have been applied to the observations to remove outliers. An additional quality control is applied to check the consistency of the error statistics estimation model at each observing station and for each hour. The error statistics are further tuned "on the fly" using a χ2 (chi-square) diagnostic, a procedure which verifies significantly better than without tuning. Successful cross-validation experiments were performed with an OA set-up using 90% of observations to build the objective analysis and with the remainder left out as an independent set of data for verification purposes. Furthermore, comparisons with other external sources of information (global models and PM2.5 satellite surface derived measurements) show reasonable agreement. The multi-year analyses obtained provide relatively high precision with an absolute yearly averaged systematic error of less than 0.6 ppbv (parts per billion by volume) and 0.7 μg m−3 (micrograms per cubic meter) for ozone and PM2.5 respectively and a random error generally less than 9 ppbv for ozone and under 12 μg m−3 for PM2.5. In this paper, we focus on two applications: (1) presenting long term averages of objective analysis and analysis increments as a form of summer climatology and (2) analyzing long term (decadal) trends and inter-annual fluctuations using OA outputs. Our results show that high percentiles of ozone and PM2.5 are both following a decreasing trend overall in North America with the eastern part of United States (US) presenting the highest decrease likely due to more effective pollution controls. Some locations, however, exhibited an increasing trend in the mean ozone and PM2.5 such as the northwestern part of North America (northwest US and Alberta). The low percentiles are generally rising for ozone which may be linked to increasing emissions from emerging countries and the resulting pollution brought by the intercontinental transport. After removing the decadal trend, we demonstrate that the inter-annual fluctuations of the high percentiles are significantly correlated with temperature fluctuations for ozone and precipitation fluctuations for PM2.5. We also show that there was a moderately significant correlation between the inter-annual fluctuations of the high percentiles of ozone and PM2.5 with economic indices such as the Industrial Dow Jones and/or the US gross domestic product growth rate.

scholarly journals Multi-year objective analyses of warm season ground-level ozone and PM<sub>2.5</sub> over North America using real-time observations and Canadian operational air quality models

2014 ◽  
Vol 14 (4) ◽  
pp. 1769-1800 ◽  
Author(s):  
A. Robichaud ◽  
R. Ménard
Keyword(s):  
Air Quality ◽  
North America ◽  
Warm Season ◽  
Ground Level ◽  
The United States ◽  
Error Statistics ◽  
Spatiotemporal Resolution ◽  
Ground Level Ozone ◽  
Air Quality Forecast

Abstract. Multi-year objective analyses (OA) on a high spatiotemporal resolution for the warm season period (1 May to 31 October) for ground-level ozone and for fine particulate matter (diameter less than 2.5 microns (PM2.5)) are presented. The OA used in this study combines model outputs from the Canadian air quality forecast suite with US and Canadian observations from various air quality surface monitoring networks. The analyses are based on an optimal interpolation (OI) with capabilities for adaptive error statistics for ozone and PM2.5 and an explicit bias correction scheme for the PM2.5 analyses. The estimation of error statistics has been computed using a modified version of the Hollingsworth–Lönnberg (H–L) method. The error statistics are "tuned" using a χ2 (chi-square) diagnostic, a semi-empirical procedure that provides significantly better verification than without tuning. Successful cross-validation experiments were performed with an OA setup using 90% of data observations to build the objective analyses and with the remainder left out as an independent set of data for verification purposes. Furthermore, comparisons with other external sources of information (global models and PM2.5 satellite surface-derived or ground-based measurements) show reasonable agreement. The multi-year analyses obtained provide relatively high precision with an absolute yearly averaged systematic error of less than 0.6 ppbv (parts per billion by volume) and 0.7 μg m−3 (micrograms per cubic meter) for ozone and PM2.5, respectively, and a random error generally less than 9 ppbv for ozone and under 12 μg m−3 for PM2.5. This paper focuses on two applications: (1) presenting long-term averages of OA and analysis increments as a form of summer climatology; and (2) analyzing long-term (decadal) trends and inter-annual fluctuations using OA outputs. The results show that high percentiles of ozone and PM2.5 were both following a general decreasing trend in North America, with the eastern part of the United States showing the most widespread decrease, likely due to more effective pollution controls. Some locations, however, exhibited an increasing trend in the mean ozone and PM2.5, such as the northwestern part of North America (northwest US and Alberta). Conversely, the low percentiles are generally rising for ozone, which may be linked to the intercontinental transport of increased emissions from emerging countries. After removing the decadal trend, the inter-annual fluctuations of the high percentiles are largely explained by the temperature fluctuations for ozone and to a lesser extent by precipitation fluctuations for PM2.5. More interesting is the economic short-term change (as expressed by the variation of the US gross domestic product growth rate), which explains 37% of the total variance of inter-annual fluctuations of PM2.5 and 15% in the case of ozone.

scholarly journals AIR QUALITY MONITORING AND DATA MANAGEMENT IN GERMANY – STATUS QUO AND SUGGESTIONS FOR IMPROVEMENT

2020 ◽  
Vol XLIV-4/W2-2020 ◽  
pp. 37-43
Author(s):  
L. Petry ◽  
H. Herold ◽  
G. Meinel ◽  
T. Meiers ◽  
I. Müller ◽  
...  
Keyword(s):  
Decision Making ◽  
Air Quality ◽  
Management Practices ◽  
System Development ◽  
Ground Level ◽  
Ground Level Ozone ◽  
Novel Approach ◽  
Depth Analysis ◽  
New Generation

Abstract. This paper proposes a novel approach to facilitate air quality aware decision making and to support planning actors to take effective measures for improving the air quality in cities and regions. Despite many improvements over the past decades, air pollutants such as particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3) pose still one of the major risks to human health and the environment. Based on both a general analysis of the air quality situation and regulations in the EU and Germany as well as an in-depth analysis of local management practices requirements for better decision making are identified. The requirements are used to outline a system architecture following a co-design approach, i.e., besides scientific and industry partners, local experts and administrative actors are actively involved in the system development. Additionally, the outlined system incorporates two novel methodological strands: (1) it employs a deep neural network (DNN) based data analytics approach and (2) makes use of a new generation of satellite data, namely Sentinel-5 Precursor (Sentinel-5P). Hence, the system allows for providing areal and high-resolution (e.g., street-level) real-time and forecast (up to 48 hours) data to inform decision makers for taking appropriate short-term measures, and secondly, to simulate air quality under different planning options and long-term actions such as modified traffic flows and various urban layouts.

scholarly journals An analysis of long-term regional-scale ozone simulations over the Northeastern United States: variability and trends

2011 ◽  
Vol 11 (2) ◽  
pp. 567-582 ◽  
Author(s):  
C. Hogrefe ◽  
W. Hao ◽  
E. E. Zalewsky ◽  
J.-Y. Ku ◽  
B. Lynn ◽  
...  
Keyword(s):  
Air Quality ◽  
Boundary Conditions ◽  
Interannual Variability ◽  
Ground Level ◽  
Lateral Boundary ◽  
Ground Level Ozone ◽  
Lateral Boundary Conditions ◽  
Ozone Precursor ◽  
Time Invariant

Abstract. This study presents the results from two sets of 18-year air quality simulations over the Northeastern US performed with a regional photochemical modeling system. These two simulations utilize different sets of lateral boundary conditions, one corresponding to a time-invariant climatological vertical profile and the other derived from monthly mean concentrations extracted from archived ECHAM5-MOZART global simulations. The objective is to provide illustrative examples of how model performance in several key aspects – trends, intra- and interannual variability of ground-level ozone, and ozone/precursor relationships – can be evaluated against available observations, and to identify key inputs and processes that need to be considered when performing and improving such long-term simulations. To this end, several methods for comparing observed and simulated trends and variability of ground level ozone concentrations, ozone precursors and ozone/precursor relationships are introduced. The application of these methods to the simulation using time-invariant boundary conditions reveals that the observed downward trend in the upper percentiles of summertime ozone concentrations is captured by the model in both directionality and magnitude. However, for lower percentiles there is a marked disagreement between observed and simulated trends. In terms of variability, the simulations using the time-invariant boundary conditions underestimate observed inter-annual variability by 30%–50% depending on the percentiles of the distribution. The use of boundary conditions from the ECHAM5-MOZART simulations improves the representation of interannual variability but has an adverse impact on the simulated ozone trends. Moreover, biases in the global simulations have the potential to significantly affect ozone simulations throughout the modeling domain, both at the surface and aloft. The comparison of both simulations highlights the significant impact lateral boundary conditions can have on a regional air quality model's ability to simulate long-term ozone variability and trends, especially for the lower percentiles of the ozone distribution.

scholarly journals An analysis of long-term regional-scale ozone simulations over the Northeastern United States: variability and trends

2010 ◽  
Vol 10 (10) ◽  
pp. 23045-23090 ◽  
Author(s):  
C. Hogrefe ◽  
W. Hao ◽  
E. E. Zalewsky ◽  
J.-Y. Ku ◽  
B. Lynn ◽  
...  
Keyword(s):  
Air Quality ◽  
Boundary Conditions ◽  
Interannual Variability ◽  
Ground Level ◽  
Lateral Boundary ◽  
Ground Level Ozone ◽  
Lateral Boundary Conditions ◽  
Ozone Precursor ◽  
Time Invariant

Abstract. This study presents the results from two sets of 18-year air quality simulations over the Northeastern US performed with a regional photochemical modeling system. These two simulations utilize different sets of lateral boundary conditions, one corresponding to a time-invariant climatological vertical profile and the other derived from monthly mean concentrations extracted from archived ECHAM5-MOZART global simulations. The objective is to provide illustrative examples of how model performance in several key aspects – trends, intra- and interannual variability of ground-level ozone, and ozone/precursor relationships – can be evaluated against available observations, and to identify key inputs and processes that need to be considered when performing and improving such long-term simulations. To this end, several methods for comparing observed and simulated trends and variability of ground level ozone concentrations, ozone precursors and ozone/precursor relationships are introduced. The application of these methods to the simulation using time-invariant boundary conditions reveals that the observed downward trend in the upper percentiles of summertime ozone concentrations is captured by the model in both directionality and magnitude. However, for lower percentiles there is a marked disagreement between observed and simulated trends. In terms of variability, the simulations using the time-invariant boundary conditions simulations underestimate observed inter-annual variability by 30–50% depending on the percentiles of the distribution. In contrast, the use of boundary conditions from the ECHAM5-MOZART simulations improves the representation of interannual variability. However, biases in the global simulations have the potential to significantly affect ozone simulations throughout the modeling domain, both at the surface and aloft. The comparison of both simulations highlights the significant impact lateral boundary conditions can have on a regional air quality model's ability to simulate long-term ozone variability and trends, especially for the lower percentiles of the ozone distribution.

scholarly journals Ozone source apportionment during peak summer events over southwestern Europe

2019 ◽  
Vol 19 (8) ◽  
pp. 5467-5494 ◽  
Author(s):  
María Teresa Pay ◽  
Gotzon Gangoiti ◽  
Marc Guevara ◽  
Sergey Napelenok ◽  
Xavier Querol ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Ad Hoc ◽  
Quantitative Estimation ◽  
Vertical Mixing ◽  
Ground Level ◽  
Anthropogenic Activity ◽  
Target Value ◽  
Air Quality Forecast ◽  
Mediterranean Countries

Abstract. It is well established that in Europe, high O3 concentrations are most pronounced in southern/Mediterranean countries due to the more favourable climatological conditions for its formation. However, the contribution of the different sources of precursors to O3 formation within each country relative to the imported (regional and hemispheric) O3 is poorly quantified. This lack of quantitative knowledge prevents local authorities from effectively designing plans that reduce the exceedances of the O3 target value set by the European air quality directive. O3 source attribution is a challenge because the concentration at each location and time results not only from local biogenic and anthropogenic precursors, but also from the transport of O3 and precursors from neighbouring regions, O3 regional and hemispheric transport and stratospheric O3 injections. The main goal of this study is to provide a first quantitative estimation of the contribution of the main anthropogenic activity sectors to peak O3 events in Spain relative to the contribution of imported (regional and hemispheric) O3. We also assess the potential of our source apportionment method to improve O3 modelling. Our study applies and thoroughly evaluates a countrywide O3 source apportionment method implemented in the CALIOPE air quality forecast system for Spain at high resolution (4 × 4 km2) over a 10-day period characterized by typical summer conditions in the Iberian Peninsula (IP). The method tags both O3 and its gas precursor emissions from source sectors within one simulation, and each tagged species is subject to the typical physico-chemical processes (advection, vertical mixing, deposition, emission and chemistry) as the actual conditions remain unperturbed. We quantify the individual contributions of the largest NOx local sources to high O3 concentrations compared with the contribution of imported O3. We show, for the first time, that imported O3 is the largest input to the ground-level O3 concentration in the IP, accounting for 46 %–68 % of the daily mean O3 concentration during exceedances of the European target value. The hourly imported O3 increases during typical northwestern advections (70 %–90 %, 60–80 µg m−3), and decreases during typical stagnant conditions (30 %–40 %, 30–60 µg m−3) due to the local NO titration. During stagnant conditions, the local anthropogenic precursors control the O3 peaks in areas downwind of the main urban and industrial regions (up to 40 % in hourly peaks). We also show that ground-level O3 concentrations are strongly affected by vertical mixing of O3-rich layers present in the free troposphere, which result from local/regional layering and accumulation, and continental/hemispheric transport. Indeed, vertical mixing largely explains the presence of imported O3 at ground level in the IP. Our results demonstrate the need for detailed quantification of the local and remote contributions to high O3 concentrations for local O3 management, and show O3 source apportionment to be an essential analysis prior to the design of O3 mitigation plans in any non-attainment area. Achieving the European O3 objectives in southern Europe requires not only ad hoc local actions but also decided national and European-wide strategies.

Effects of rainfall, moisture stress, and stocking rate on the persistence of white clover over 30 years

1995 ◽  
Vol 35 (7) ◽  
pp. 1039 ◽  
Author(s):  
KJ Hutchinson ◽  
KL King ◽  
DR Wilkinson
Keyword(s):  
Soil Water ◽  
White Clover ◽  
Warm Season ◽  
Late Summer ◽  
Ground Level ◽  
Moisture Stress ◽  
Smoothing Splines ◽  
Water Model ◽  
Stocking Rate

The effects of spring rainfall, critical levels of summer moisture stress, and sheep stocking rates on the persistence of white clover (Trifolium repens cv. Huia) have been evaluated in a 30-year experiment (1964-93) based on sown, well-fertilised pasture. Plant species presence was measured each year as basal cover using a vertical 10-pin frame. Hits at ground level from 800 points/plot were recorded in late September on duplicate plots, which were set-stocked at 3 rates (10, 20 reduced to 15, 30 reduced to 20 d.s.e./ha). A soil-water model based on rainfall and tank evaporation was calibrated against on-site soil water measurements (0-260 mm) and used to predict soil water (mm) for weekly time steps over 30 years. Smoothing of long-term rainfall data (SYSTAT, Lowess) showed an overall decline in warm-season rainfall (October-March), which was punctuated by above-average (1969-74) and average runs of years (1983-90). Flexible smoothing splines (SAS) were used to indicate patterns of yearly white clover presence. For all stocking treatments, there were significant declines in the presence of white clover over 3 decades. At the highest stocking rate, the recovery of white clover following the 1965 drought was poor. Late summer (January-March) moisture stress, defined as the number of weeks when soil water (0-260 mm) was <15 mm, was critical in determining white clover presence in the following spring (September). Rainfall received from October to December generally had a positive effect. These climate-based relationships reinforce the importance of stolon growth and survival as a regenerative strategy for white clover. However, over the 30 years, the species showed decreasing resilience post drought, which suggests a long-term failure of seed-based regeneration. Annual rates of soil nitrogen build-up ranged from 29 to 54 kg N/ha.year and were poorly related to white clover presence in the stocking treatments. Governing mechanisms, based on interactions between seasonal moisture stress, sheep stocking rate, interspecific plant competition, and seed pool dynamics, are proposed to explain the nature of long-term decline in white clover presence in well-fertilised, sown pastures in the Northern Tablelands of New South Wales.

scholarly journals Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

2009 ◽  
Vol 2 (2) ◽  
pp. 1375-1406 ◽  
Author(s):  
D. Kang ◽  
R. Mathur ◽  
S. Trivikrama Rao
Keyword(s):  
United States ◽  
Kalman Filter ◽  
Air Quality ◽  
Recursive Algorithm ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Rocky Mountain ◽  
Meteorological Model ◽  
Bias Adjustment ◽  
Air Quality Forecast

Abstract. To develop fine particular matter (PM2.5) air quality forecasts, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the pacific coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjustment forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematical improvements in forecast results across all seasons suggest that this technique can be easily adapted to perform bias-adjustment for real-time PM2.5 air quality forecasts.

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