Abstract. In this study, we developed an approach that integrated multiple
patterns of timescale for box modeling (MCMv3.3.1) to better understand the
O3–precursor relationship at multiple sites and through continuous
observations. A 5-month field campaign was conducted in the summer of
2019 to investigate the ozone formation chemistry at three sites in a major
prefecture-level city (Zibo) in Shandong Province of northern China. It was
found that the relative incremental reactivity (RIR) of major precursor
groups (e.g., anthropogenic volatile organic compounds (AVOCs), NOx) was
overall consistent in terms of timescales changed from wider to
narrower (four patterns: 5-month, monthly, weekly, and daily) at each
site, though the magnitudes of RIR varied at different sites. The time
series of the photochemical regime (using RIRNOx / RIRAVOC as an
indicator) in weekly or daily patterns further showed a synchronous temporal
trend among the three sites, while the magnitude of RIRNOx / RIRAVOC
was site-to-site dependent. The derived RIR ranking (top 10) of individual
AVOC species showed consistency between three patterns (i.e., 5-month,
monthly, and weekly). It was further found that the campaign-averaging
photochemical regimes showed overall consistency in the sign but
non-negligible variability among the four patterns of timescale, which was
mainly due to the embedded uncertainty in the model input dataset when averaging
individual daily patterns into different timescales. This implies that
utilizing narrower timescales (i.e., daily pattern) is useful for deriving
reliable and robust O3–precursor relationships. Our results highlight
the importance of quantifying the impact of different timescales to
constrain the photochemical regime, which can formulate more accurate
policy-relevant guidance for O3 pollution control.
Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m−3 yr−1 on average for the city, while it is 1.52–5.95 μg m−3 yr−1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m−3 and +3.77 μg m−3. This difference in trends between the city and specific stations implied that the O3 precursor’s mitigation strategy should be more precise to improve its practical effects.
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