Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI

Source characterization of volatile organic compounds at Carlsbad Caverns National Park

Carlsbad Caverns National Park (CAVE), located in southeastern New Mexico, experiences elevated ground-level ozone (O3) exceeding the National Ambient Air Quality Standard (NAAQS) of 70 ppbv. It is situated adjacent to the Permian Basin, one of the largest oil and gas (O&G) producing regions in the US. In 2019, the Carlsbad Caverns Air Quality Study (CarCavAQS) was conducted to examine impacts of different sources on ozone precursors, including nitrogen oxides (NOx) and volatile organic compounds (VOCs). Here, we use positive matrix factorization (PMF) analysis of speciated VOCs to characterize VOC sources at CAVE during the study. Seven factors were identified. Three factors composed largely of alkanes and aromatics with different lifetimes were attributed to O&G development and production activities. VOCs in these factors were typical of those emitted by O&G operations. Associated residence time analyses (RTA) indicated their contributions increased in the park during periods of transport from the Permian Basin. These O&G factors were the largest contributor to VOC reactivity with hydroxyl radicals (62%). Two PMF factors were rich in photochemically generated secondary VOCs; one factor contained species with shorter atmospheric lifetimes and one with species with longer lifetimes. RTA of the secondary factors suggested impacts of O&G emissions from regions farther upwind, such as Eagle Ford Shale and Barnett Shale formations. The last two factors were attributed to alkenes likely emitted from vehicles or other combustion sources in the Permian Basin and regional background VOCs, respectively.Implications: Carlsbad Caverns National Park experiences ground-level ozone exceeding the National Ambient Air Quality Standard. Volatile organic compounds are critical precursors to ozone formation. Measurements in the Park identify oil and gas production and development activities as the major contributors to volatile organic compounds. Emissions from the adjacent Permian Basin contributed to increases in primary species that enhanced local ozone formation. Observations of photochemically generated compounds indicate that ozone was also transported from shale formations and basins farther upwind. Therefore, emission reductions of volatile organic compounds from oil and gas activities are important for mitigating elevated O3 in the region.

Assessment of variations in air quality in cities of Ecuador in relation to the lockdown due to the COVID-19 pandemic

This study analyzes the effect of lockdown due to COVID-19 on the spatiotemporal variability of ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) concentrations in different provinces of continental Ecuador using satellite information from Sentinel - 5P. The statistical analysis includes data from 2018 to March 2021 and was performed based on three periods defined a priori: before, during, and after lockdown due to COVID-19, focusing on the provinces with the highest concentrations of the studied gases (hotspots). The results showed a significant decrease in NO₂ concentrations during the COVID-19 lockdown period in all the study areas: the Metropolitan District of Quito (DMQ) and the provinces of Guayas and Santo Domingo de los Tsáchilas. In the period after lockdown, NO₂ concentrations increased by over 20% when compared to the pre-lockdown period, which may be attributable to a shift towards private transportation due to health concerns. On the other hand, SO₂ concentrations during the lockdown period showed irregular, non-significant variations; however, increases were observed in the provinces of Chimborazo, Guayas, Santa Elena, and Morona Santiago, which could be partly attributed to the eruptive activity of the Sangay volcano during 2019-2020. Conversely, O₃ concentrations increased by 2-3% in the study areas; this anomalous behavior could be attributed to decreased levels of NO_x, which react with ozone, reducing its concentration. Finally, satellite data validation using the corresponding data from monitoring stations in the DMQ showed correlation values of 0.9 for O₃ data and 0.7 for NO₂ data, while no significant correlation was found for SO₂.

Modeling the biogenic isoprene emission and its impact on ozone pollution in Zhejiang province, China

Isoprene is the most abundantly emitted biogenic volatile organic compound (BVOC), which plays an essential role in producing tropospheric ozone (O₃). However, the simulations of isoprene emissions have not been sufficiently verified over Yangtze River Delta (YRD), and few studies have specifically addressed its impact on O₃ formation. In this study, we simulated the isoprene emissions in Zhejiang Province (ZJ), a region with the largest BVOC emission in YRD, in August 2020 using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the latest Moderate Resolution Imaging Spectroradiometer (MODIS) products, and investigated its contributions to O₃ using the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model. The model has a good performance on isoprene simulations over urban and suburban areas, with mean biases of -0.16-0.12 ppb, but underestimated the concentrations at forest sites (mainly due to bamboo). Regionally, the simulated formaldehyde concentrations over forests agree well with the satellite observations. In August 2020, the total isoprene emission in ZJ was 125.1 GgC, with higher emissions in western ZJ and relatively lower emissions in eastern coastal regions. The spatial pattern of isoprene concentrations is similar to its emissions, and the maximum daytime average concentrations are above 3.5 ppb. The spatial pattern of its contribution to daily maximum 8 h average O₃ concentrations is significantly different from the emissions and concentrations, which shows a higher impact in northern ZJ (>6 ppb) and relatively lower impact in southern ZJ (1-3 ppb). The mean contribution over ZJ is 8.9 %, with daily variation in the range of 3.1 % to 13.4 %. For different cities, the monthly mean contribution is in the range of 4.6 % to 14.3 %, and the maximum daily contribution reaches about 25 %. These findings help understand the summertime O₃ pollution in ZJ and the YRD region of China.

Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper xaddresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.