Investigation of the Successive Ozone Episodes in the El Paso–Juarez Region in the Summer of 2017

Analysis of Vertical Distribution Changes and Influencing Factors of Tropospheric Ozone in China from 2005 to 2020 Based on Multi-Source Data

The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCC_LT) showed an increasing trend, with an average annual increase of 0.143 DU. The OCC in the middle troposphere showed a downward trend, with an average annual decrease of 0.091 DU. There was a significant negative correlation between the ozone changes in the two layers. The monthly average results show that the peak values of OCC_LT occur in May or June, the middle troposphere is significantly influenced by topographic conditions, and the upper troposphere is mainly affected by latitude. Analysis based on multi-source data shows that the reduction in nitrogen oxides (NO_x) and the increase in volatile organic compounds (VOCs) weakened the titration of ozone generation, resulting in the increase in OCC_LT. The increase in vegetation is closely related to the increase in OCC_LT, with a correlation coefficient of up to 0.875. The near-surface temperature increased significantly, which strengthened the photochemical reaction of ozone. In addition, the increase in boundary layer height also plays a positive role in the increase in OCC_LT.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

Winter and Wildfire Season Optical Characterization of Black and Brown Carbon in the El Paso-Ciudad Juárez Airshed

Black (EBC) and Brown (BrC) Carbon are ubiquitous constituents of atmospheric particulate matter that affect people’s health, disrupt ecosystems, and modulate local and global climate. Tracking the local deposition and sources of these aerosol particles is essential to better understanding their multidimensional environmental impact. The main goal of the current study is to measure the absorption coefficient (Babs) of particles within the Planetary Boundary Layer (PBL) of the El Paso (US)–Ciudad Juárez (Mexico) airshed and assess the contribution of black and brown carbon particles to the optical absorption. Measurements were taken during a summer, wildfire, and winter season to evaluate the optical properties of BC and non-volatile BrC. The winter season presented a variation from the background Babs in the late evening hours (3:00 PM to midnight) due to an increase in biomass burning driven by lower temperatures. The wildfire season presents the greatest variation in the Babs from the background absorption due to EBC- and BrC-rich smoke plumes arriving at this region from the US West seasonal wildfires. It was found that the international bridges’ vehicular traffic, waiting time to cross back and forth between both cities, added to other local anthropogenic activities, such as brick kiln emissions in Ciudad Juarez, have created a background of air pollution in this region. These pollutants include carbon monoxide, sulfur dioxide, nitrogen and nitric oxides, coarse and fine particulate matter dominated by BC and BrC. The absorption coefficients due to EBC and BrC of this background constitute what we have called a baseline EBC and BrC. Aided by two photoacoustic Extinctiometers (PAX), operating at 405 nm and 870 nm wavelengths, connected to a 340 °C thermal denuder to remove volatile organics, the optical properties were documented and evaluated to identify the impact of long-range transported emissions from western wildfires. The Single Scattering Albedo and the Absorption Ångstrom exponent were calculated for the winter and summer season. The Angstrom exponent showed a decrease during the wildfire events due to the aging process. The High-Resolution Rapid Refresh Smoke model, HRRR, and the Hybrid Single-Particle Lagrangian Integrated Trajectory model, HYSPLIT, were used to estimate the sources of the particles. In addition, a Vaisala Ceilometer was employed to study the vertical profile of particulate matter within the planetary boundary layer.

The Dynamical Role of the Chesapeake Bay on the Local Ozone Pollution Using Mesoscale Modeling—A Case Study

This study investigated the dynamic influence of the Chesapeake Bay (CB) on local ozone (O3) concentration and distribution using a weather forecasting model. The Weather Research and Forecasting model coupled with Chemistry (WRF–Chem) was employed to simulate O3 production and transportation near the CB. Baseline (water) as well as sensitivity (nowater) model experiments of bay circulation were carried out with and without bay water by changing the water surface from water to land (loam). First, the model performance simulating O3 was evaluated using the baseline experiment results and AirNow surface wind and O3 observations. The results showed that the model overestimates surface O3 by up to 20–30%. Further, the comparisons of the baseline and sensitivity experiments revealed higher O3 mixing ratios, primarily due to the resulting bay breeze circulation. These increases, after considering model overestimation, represent a mean bay dynamics circulation-induced contribution of up to 10% at night and 5% during the day. Furthermore, the boundary layer over northern CB, where it is at its narrowest width, was higher (by 1.2 km on average) during daytime due to higher surface temperatures observed. The boundary layer depth difference between the northern, central, and southern regions of the bay leads to a differential in the role of bay circulation dynamics in the observed O3 increase. The relatively wider swath of water surface over southern CB resulted in a lower boundary layer depth and stronger breeze circulation and this circulation contributed to O3 concentrations. Moreover, since the case selected has a minimal bay breeze circulation, the associated surface ozone enhancements represent what is expected at least at a minimum.

Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.