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As an important irritant trace gas in the atmosphere, ammonia (NH3) significantly impacts human health and environment. Bottom-up emission inventories are widely used to estimate ammonia emissions and their geographical distributions over China. However, high uncertainties are still associated with emission inventories due to inaccurate emission factors used. The Differential Evolution (DE) algorithm is a population-based stochastic optimization algorithm used to solve complicated optimization problems. We quantify NH3 emissions and lifetime from Infrared Atmospheric Sounding Interferometer (IASI) NH3 observations together with MERRA-2 wind fields based on the DE algorithm. Two inland cities, Urumchi and Golmud in China, are chosen to study of the NH3 emissions based on the distributions of NH3 total columns and wind fields. The NH3 emissions rate estimated is about 5.84 × 10−11 and 4.19 × 10−11 kg·m−2s−1 in Urumchi and in the Golmud area from May to September from 2008 to 2023, respectively. The lifetime of NH3 estimated in the two areas is 4.31 and 9.19 h, respectively. We compare the NH3 emissions and lifetime estimated in this study with the values in other studies, and the results show the reliability of the method used. This work is one of few quantitative studies of NH3 emissions from cities using satellite observations in China. [ABSTRACT FROM AUTHOR]

The various industries, sectors, and citizens' daily lives have undergone significant changes after the outbreak of the COVID-19 pandemic. The researchers collected and analyzed PM2.5 samples including secondary inorganic ions (SO42−, NO3−, and NH4+, namely SNA), organic carbon (OC), elemental carbon (EC), and other 16 metal elements in Beijing in 2019 (before the pandemic) and 2020 (after the pandemic). The particulate matter (PM2.5) concentration in the autumn and winter of 2020 is 21.16 µg/m3 and 14.05 µg/m3 lower than in 2019, respectively. The contribution of six sources of pollution, including coal combustion, secondary sources, transportation-related sources, dust, Industrial I, and Industrial II, were analyzed using the Positive Matrix Factorization (PMF) model. Due to the impacts of the COVID-19 pandemic, more and more people are choosing private transportation, such as private cars, instead of public transportation. As a result, the contribution of PM2.5 pollution related to transportation increased after the pandemic. The metal elements measured during the sampling period represent only a very small fraction (1%) of PM2.5. However, their health risk to humans cannot be ignored because of the toxicity of some metallic elements, and the carcinogenic risks induced by metal elements in PM2.5 exceeded the safety threshold (>10−6) during the autumn and winter of 2019 and 2020. Arsenic (As) contributes the most to carcinogenic risk, so controlling arsenic emissions is the primary approach to reducing cancer risk in Beijing. Considering the contribution to the health risk from various sources obtained in PMF, coal combustion is the most significant contributor to cancer risk. Therefore, serious consideration should be given to controlling coal combustion at the local and regional levels to reduce health risks in Beijing. [ABSTRACT FROM AUTHOR]