A new method for assessing the efficacy of emission control strategies

Abstract

Regional-scale air quality models and observations at routine air quality monitoring sites are used to determine attainment/non-attainment of the ozone air quality standard in the United States. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Because of the stochastic nature of the atmosphere, the same meteorological conditions would not prevail in the future year. Therefore, we use multi-decadal observations to develop a new method for estimating the confidence bounds for the future ozone design value (based on the 4 th highest value in the daily maximum 8-hr ozone concentration time series, DM8HR) for each emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at all monitoring sites in the contiguous United States. To this end, we spectrally decompose the observed DM8HR ozone time series covering the period from 1981 to 2014 using the Kolmogorov-Zurbenko (KZ) filter and examine the variability in the relative strengths of the short-term variations (induced by synoptic-scale weather fluctuations; referred to as synoptic component, SY) and the long-term component (dictated by changes in emissions, seasonality and other slow-changing processes such as climate change; referred to as baseline component, BL). Results indicate that combining the projected change in the ozone baseline level with the adjusted synoptic forcing in historical ozone observations enables us to provide a probabilistic assessment of the efficacy of a selected emissions control strategy in complying with the ozone standard in future years. In addition, attainment demonstration is illustrated with a realworld application of the proposed methodology by using air quality model simulations, thereby helping build confidence in the use of regional-scale air quality models for supporting regulatory policies.

On the limit to the accuracy of regional-scale air quality models

“…The scientific discussion on modeling uncertainty goes back more than 3 decades with the current practice including data assimilation, ensemble modeling, and model performance evaluation (e.g., Fox, 1981Fox, , 1984Lamb, 1984;Demerjian, 1985;Oreskes et al, 1994;Pielke, 1998;Lewellen and Sykes, 1989;Lee et al, 1997;Carmichael et al, 2008;Hogrefe et al, 2001a, b;Biswas and Rao, 2001;Grell and Baklanov, 2011;Gilliam et al, 2006;Herwehe et al, 2011;Baklanov et al, 2014;Bocquet et al, 2015;Solazzo and Galmarini, 2015a;Ying and Zhang, 2018;McNider and Pour-Biazar, 2020;Stockwell et al, 2020). While ever-improving process knowledge and increasing computational power will continue to help reduce the structural and parametric uncertainties in air quality models, the inherent uncertainty associated with our inability to properly characterize the stochastic nature of the atmosphere will always result in some mismatch between the model results and measurements; this could lead to speculation on the inferred accuracy of the future states simulated by the regional-scale air quality models (Dennis et al, 2010;Rao et al, 2011a;Porter et al, 2015;Astitha et al, 2017;Luo et al, 2019). The sensitivity of model results to meteorology, chemical mechanisms, and emissions has been examined in numerous studies (e.g., Vautard et al, 2012;Sarwar et al, 2013;Pierce et al, 2010;Napelenok et al, 2011;.…”

On the Limit to the Accuracy of Regional-Scale Air Quality Models

“…The scientific discussion on modeling uncertainty reduction goes back more than three decades with the current practice including data assimilation, ensemble modeling, and model performance evaluation (e.g., Fox, 1981Fox, , 1984Lamb, 1984;Pielke, 1998;Lewellen and Sykes, 1989;Lee et al, 1997;15 Carmichael et al, 2008;Hogrefe et al, 2001aHogrefe et al, , 2001bGrell and Baklanov, 2011;Gilliam et al, 2006;Baklanov et al, 2014;Bocquet et al, 2015). While ever-improving process knowledge and increasing computational power will continue to help reduce the structural and parametric uncertainties in air quality models, the inherent uncertainty cannot be eliminated because our inability to properly characterize the stochastic nature of the atmosphere will always result in some mismatch between the model results and measurements; this could lead to speculation on the inferred accuracy of the future states simulated by the 20 regional air quality models (Porter et al, 2015;Astitha et al, 2017;Luo et al, 2019).…”

“…Further, the KZ filtering is a simple method and works well in the presence of missing data (Hogrefe et al, 2003). In this study, we used the KZ (5,5) with a window size of 5 days and 5 iterations in the same manner as in Porter et al (2015), Rao et al (2011), andLuo et al (2019). The size of the window and the number of iterations determine the desired 25 scale separation.…”

“…Once the baseline is separated from the original DM8HR time series from all monitoring stations, then the synoptic forcing in the historical ozone 5 time series data is used to estimate the variability in ozone concentrations that can be expected because of the chaotic/stochastic nature of the atmosphere by taking into account the relationship between the strength of synoptic forcing and mean of baseline ozone at each location over CONUS. This methodology was applied to both measured and modeled ozone concentrations (see details in Luo et al, 2019). Whereas the objective of Luo et al (2019) was on transforming the deterministic modeling results into a probabilistic framework for assessing the efficacy of different emission control strategies in achieving compliance with 10 the ozone standard, this paper is aimed at quantifying the errors to be expected at each monitoring site over CONUS even from "perfect" regional ozone models driven with "perfect" input data from the ever-present stochastic nature of the atmosphere.…”

“…This methodology was applied to both measured and modeled ozone concentrations (see details in Luo et al, 2019). Whereas the objective of Luo et al (2019) was on transforming the deterministic modeling results into a probabilistic framework for assessing the efficacy of different emission control strategies in achieving compliance with 10 the ozone standard, this paper is aimed at quantifying the errors to be expected at each monitoring site over CONUS even from "perfect" regional ozone models driven with "perfect" input data from the ever-present stochastic nature of the atmosphere.…”