Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world’s most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a⁻¹, representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data–based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.

Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated governmental control measures is crucial to understand its impacts on economy, air pollution, and society. We used the WRF-GC model and the tropospheric NO₂ column observations retrieved by the TROPOMI instrument to derive the top-down NOx emission change estimation between the three periods: P1 (January 1st to January 22nd, 2020), P2 (January 23rd, Wuhan lockdown, to February 9th, 2020), and P3 (February 10th, back-to-work day, to March 12th, 2020). We found that NOx emissions in East China averaged during P2 decreased by 50% compared to those averaged during P1. The NOx emissions averaged during P3 increased by 26% compared to those during P2. Most provinces in East China gradually regained some of their NOx emissions after February 10, the official back-to-work day, but NOx emissions in most provinces have not yet to return to their previous levels in early January. NOx emissions in Wuhan, the first epicenter of COVID-19, had no sign of emission recovering by March 12. A few provinces, such as Zhejiang and Shanxi, have recovered fast, with their averaged NOx emissions during P3 almost back to pre-lockdown levels.

By 15 April 2020, more than 1.5 billion students worldwide experienced school closures in an effort to slow the spread of a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), during the worldwide coronavirus disease 2019 (COVID-19) pandemic. These interruptions in formal in-person educational experiences caused adverse consequences on school-age children’s academic outcomes. Using a pre-existing database, we calculated changes in children’s reading ability without formal education (i.e., the summer months). The resultant models predicted that the rate of reading ability gain in kindergarten children during COVID-19 school closures without formal in-person education will decrease 66% (2.46 vs. 7.17 points/100 days), compared to the business-as-usual scenario, resulting in a 31% less reading ability gain from 1 January 2020 to 1 September 2020. Additionally, the model predicted that kindergarten children who have books read to them daily would have 2.3 points less loss (42%) compared to those who do not, who are predicted to have a 5.6-point loss during the same time period. Even though reading books to children will not substitute the critical role of formal education in teaching children how to read, families, educators, and policy makers can promote this simple strategy to facilitate and maintain reading ability gain during school closures, which may be a common occurrence as nations see the public health benefits of physical distancing for the current and future pandemic outbreaks.

Recent studies have revealed a significant influx of anthropogenic aerosol from South Asia to the Himalayas and Tibetan Plateau (TP) during pre-monsoon period. In order to characterize the chemical composition, sources, and transport processes of aerosol in this area, we carried out a field study during June 2015 by deploying a suite of online instruments including an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-AMS) and a multi-angle absorption photometer (MAAP) at Nam Co station (90 ^∘ 57 ^′ E, 30 ^∘ 46 ^′ N; 4730 m a.s.l.) at the central of the TP. The measurements were made at a period when the transition from pre-monsoon to monsoon occurred. The average ambient mass concentration of submicron particulate matter (PM ₁ ) over the whole campaign was ∼ 2.0 µg m ⁻³ , with organics accounting for 68 %, followed by sulfate (15 %), black carbon (8 %), ammonium (7 %), and nitrate (2 %). Relatively higher aerosol mass concentration episodes were observed during the pre-monsoon period, whereas persistently low aerosol concentrations were observed during the monsoon period. However, the chemical composition of aerosol during the higher aerosol concentration episodes in the pre-monsoon season was on a case-by-case basis, depending on the prevailing meteorological conditions and air mass transport routes. Most of the chemical species exhibited significant diurnal variations with higher values occurring during afternoon and lower values during early morning, whereas nitrate peaked during early morning in association with higher relative humidity and lower air temperature. Organic aerosol (OA), with an oxygen-to-carbon ratio (O ∕ C) of 0.94, was more oxidized during the pre-monsoon period than during monsoon (average O ∕ C ratio of 0.72), and an average O ∕ C was 0.88 over the entire campaign period, suggesting overall highly oxygenated aerosol in the central TP. Positive matrix factorization of the high-resolution mass spectra of OA identified two oxygenated organic aerosol (OOA) factors: a less oxidized OOA (LO-OOA) and a more oxidized OOA (MO-OOA). The MO-OOA dominated during the pre-monsoon period, whereas LO-OOA dominated during monsoon. The sensitivity of air mass transport during pre-monsoon with synoptic process was also evaluated with a 3-D chemical transport model.

In this study, we evaluated the variations of air quality in Lanzhou, a typical city in Northwestern China impacted by the COVID‐19 lockdown. The mass concentration and chemical composition of non‐refractory submicron particulate matter (NR‐PM₁) were determined by a high‐resolution aerosol mass spectrometer during January‐March 2020. The concentration of NR‐PM₁ dropped by 50% from before to during control period. The five aerosol components (sulfate, nitrate, ammonium, chloride, and organic aerosol [OA]) all decreased during the control period with the biggest decrease observed for secondary inorganic species (70% of the total reduction). Though the mass concentration of OA decreased during the control period, its source emissions varied differently. OA from coal and biomass burning remained stable from before to during control period, while traffic and cooking related emissions were reduced by 25% and 50%, respectively. The low concentration during the control period was attributed to the lower production rate for secondary aerosols.

With the improved spatial resolution of the Ozone Monitoring Instrument (OMI) over earlier instruments and more than 10 years of service, tropospheric NO₂ retrievals from OMI have led to many influential studies on the relationships between socioeconomic activities and NO_x emissions. Previous studies have shown that the OMI NO₂ data show different relative trends compared to in situ measurements. However, the sources of the discrepancies need further investigations. This study focuses on how to appropriately compare relative trends derived from OMI and in situ measurements. We retrieve OMI tropospheric NO₂ vertical column densities (VCDs) and obtain the NO₂ seasonal trends over the United States, which are compared with coincident in situ surface NO₂ measurements from the Air Quality System (AQS) network. The Mann–Kendall method with Sen's slope estimator is applied to derive the NO₂ seasonal and annual trends for four regions at coincident sites during 2005–2014. The OMI-based NO₂ seasonal relative decreasing trends are generally biased low compared to the in situ trends by up to 3.7 % yr⁻¹, except for the underestimation in the US Midwest and Northeast during December, January, and February (DJF). We improve the OMI retrievals for trend analysis by removing the ocean trend, using the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data in air mass factor (AMF) calculation. We apply a lightning flash filter to exclude lightning-affected data to make proper comparisons. These data processing procedures result in close agreement (within 0.3 % yr⁻¹) between in situ and OMI-based NO₂ regional annual relative trends. The remaining discrepancies may result from inherent difference between trends of NO₂ tropospheric VCDs and surface concentrations, different spatial sampling of the measurements, chemical nonlinearity, and tropospheric NO₂ profile changes. We recommend that future studies apply these procedures (ocean trend removal and MODIS albedo update) to ensure the quality of satellite-based NO₂ trend analysis and apply the lightning filter in studying surface NO_x emission changes using satellite observations and in comparison with the trends derived from in situ NO₂ measurements. With these data processing procedures, we derive OMI-based NO₂ regional annual relative trends using all available data for the US West (−2.0 % ± 0.3 yr⁻¹), Midwest (−1.8 % ± 0.4 yr⁻¹), Northeast (−3.1 % ± 0.5 yr⁻¹), and South (−0.9 % ± 0.3 yr⁻¹). The OMI-based annual mean trend over the contiguous United States is −1.5 % ± 0.2 yr⁻¹. It is a factor of 2 lower than that of the AQS in situ data (−3.9 % ± 0.4 yr⁻¹); the difference is mainly due to the fact that the locations of AQS sites are concentrated in urban and suburban regions.