Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

Summertime Clean-Background Ozone Concentrations Derived from Ozone Precursor Relationships are Lower than Previous Estimates in the Southeast United States

Background ozone in this study is defined as the amount of ozone that is not affected by the emissions of ozone precursors in the region of study and is transported from the distant troposphere or the stratosphere. It is one of the factors that must be considered in regional ozone control strategies. Different methods have been applied to define the background ozone level. We develop a new method based on the O₃-CO-HCHO relationships, which can be applied to both observation and modeling data for regions with high isoprene emission ozone, such as the Southeast United States. We make use of the extensive aircraft and surface observations in the Southeast in the summer of 2013. Compared to the diagnostic results using the relationship of O₃-NO_z (total reactive nitrogen excluding nitrogen oxides), zero-emission (model-only), and 5^th percentile methods, the new method is most consistent using observation or model data and the resulting background ozone concentrations are 4-50% lower than the other methods for field campaigns. Using this method, we find that the summertime background ozone at the surface is in the range of 10-15 ppbv in the inland areas of the Southeast, which is lower than that reported in previous studies. This background ozone tends to increase from urban centers to rural regions and from the surface to higher altitude due to changing ozone lifetime driven by anthropogenic emissions and dry deposition to the surface. The better quantification of background ozone using the new method highlights the importance of the contributions by natural emissions to ozone and the necessity to control anthropogenic emissions in ozone nonattainment areas of the Southeast.

Analysis of wintertime O3 variability using a random forest model and high-frequency observations in Zhangjiakou-an area with background pollution level of the North China Plain

The short-term health effects of ozone (O₃) have highlighted the need for high-temporal-resolution O₃ observations to accurately assess human exposure to O₃. Here, we performed 20-s resolution observations of O₃ precursors and meteorological factors to train a random forest model capable of accurately predicting O₃ concentrations. Our model performed well with an average validated R² of 0.997. Unlike in typical linear model frameworks, variable dependencies are not clearly modelled by random forest model. Thus, we conducted additional studies to provide insight into the photochemical and atmospheric dynamic processes driving variations in O₃ concentrations. At nitrogen oxides (NO_x) concentrations of 10-20 ppb, all the other O₃ precursors were in states that increased the production of O₃. Over a short timescale, nitrogen dioxide (NO₂) can almost track each high-frequency variation in O₃. Meteorological factors play a more important role than O₃ precursors do in predicting O₃ concentrations at a high temporal resolution; however, individual meteorological factors are not sufficient to track every high-frequency change in O₃. Nevertheless, the sharp variations in O₃ related to flow dynamics are often accompanied by steep temperature changes. Our results suggest that high-temporal-resolution observations, both ground-based and vertical profiles, are necessary for the accurate assessment of human exposure to O₃ and the success and accountability of the emission control strategies for improving air quality.

Global O3-CO Correlations in a Chemistry and Transport Model During July-August: Evaluation with TES Satellite Observations and Sensitivity to Input Meteorological Data and Emissions

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O₃-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O₃-CO correlations to input meteorological data. Model simulations of radionuclide tracers (²²²Rn, ²¹⁰Pb, and ⁷Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O₃ values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O₃ columns. Despite the fact that three simulations show significantly different global and regional distributions of O₃ and CO concentrations, all simulations show similar patterns of O₃-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O₃-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O₃-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O₃ and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NO_x emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O₃-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O₃-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O₃ is consumed and CO generated during the atmospheric oxidation process of isoprene under low NO_x conditions. We find that lightning NO_x emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NO_x emissions is not directly related to the emission and transport of CO. Our study concludes that O₃-CO correlations may be used effectively to constrain the sources of regional tropospheric O₃ in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

Detection of deep stratospheric intrusions by cosmogenic 35S

The extent to which stratospheric intrusions on synoptic scales influence the tropospheric ozone (O₃) levels remains poorly understood, because quantitative detection of stratospheric air has been challenging. Cosmogenic ³⁵S mainly produced in the stratosphere has the potential to identify stratospheric air masses at ground level, but this approach has not yet been unambiguously shown. Here, we report unusually high ³⁵S concentrations (7,390 atoms m^-3; ∼16 times greater than annual average) in fine sulfate aerosols (aerodynamic diameter less than 0.95 µm) collected at a coastal site in southern California on May 3, 2014, when ground-level O₃ mixing ratios at air quality monitoring stations across southern California (43 of 85) exceeded the recently revised US National Ambient Air Quality Standard (daily maximum 8-h average: 70 parts per billion by volume). The stratospheric origin of the significantly enhanced ³⁵S level is supported by in situ measurements of air pollutants and meteorological variables, satellite observations, meteorological analysis, and box model calculations. The deep stratospheric intrusion event was driven by the coupling between midlatitude cyclones and Santa Ana winds, and it was responsible for the regional O₃ pollution episode. These results provide direct field-based evidence that ³⁵S is an additional sensitive and unambiguous tracer in detecting stratospheric air in the boundary layer and offer the potential for resolving the stratospheric influences on the tropospheric O₃ level.

Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.

Air pollution, weather, and respiratory emergency room visits in two northern New England cities: an ecological time-series study

Daily emergency room (ER) visits for all respiratory (ICD-9 460-519) and asthma (ICD-9 493) were compared with daily sulfur dioxide (SO2), ozone (O3), and weather variables over the period 1998-2000 in Portland, Maine (population 248,000), and 1996-2000 in Manchester, New Hampshire (population 176,000). Seasonal variability was removed from all variables using nonparametric smoothed function (LOESS) of day of study. Generalized additive models were used to estimate the effect of elevated levels of pollutants on ER visits. Relative risks of pollutants are reported over their interquartile range (IQR, the 75th -25th percentile pollutant values). In Portland, an IQR increase in SO2 was associated with a 5% (95% CI 2-7%) increase in all respiratory ER visits and a 6% (95% CI 1-12%) increase in asthma visits. An IQR increase in O3 was associated with a 5% (95% CI 1-10%) increase in Portland asthmatic ER visits. No significant associations were found in Manchester, New Hampshire, possibly due to statistical limitations of analyzing a smaller population. The absence of statistical evidence for a relationship should not be used as evidence of no relationship. This analysis reveals that, on a daily basis, elevated SO2 and O3 have a significant impact on public health in Portland, Maine.