Accounting for meteorological effects in the detector of the charged component of cosmic rays

In this paper, we discuss the influence of meteorological effects on the data of the ground installation CARPET, which is a detector of the charged component of secondary cosmic rays (CRs). This device is designed in the P.N. Lebedev Physical Institute (LPI, Moscow, Russia) and installed at the Dolgoprudny scientific station (Dolgoprudny, Moscow region; 55.56∘ N, 37.3∘ E; geomagnetic cutoff rigidity (Rc = 2.12 GV) in 2017. Based on the data obtained in 2019–2020, the barometric and temperature correction coefficients for the CARPET installation were determined. The barometric coefficient was calculated from the data of the barometric pressure sensor included in the installation. To determine the temperature effect, we used the data of upper-air sounding of the atmosphere obtained by the Federal State Budgetary Institution “Central Aerological Observatory” (CAO), also located in Dolgoprudny. Upper-air sounds launch twice a day and can reach an altitude of more than 30 km.

Separating emission and meteorological contributions to long-term PM_2.5 trends over eastern China during 2000–2018

The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

2019–20 Australian Bushfires and Anomalies in Carbon Monoxide Surface and Column Measurements

In Australia, bushfires are a natural part of the country’s landscape and essential for the regeneration of plant species; however, the 2019–20 bushfires were unprecedented in their extent and intensity. This paper is focused on the 2019–20 Australian bushfires and the resulting surface and column atmospheric carbon monoxide (CO) anomalies around Wollongong. Column CO data from the ground-based Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) site in Wollongong are used together with surface in situ measurements. A systematic comparison was performed between the surface in situ and column measurements of CO to better understand whether column measurements can be used as an estimate of the surface concentrations. If so, satellite column measurements of CO could be used to estimate the exposure of humans to CO and other fire-related pollutants. We find that the enhancements in the column measurements are not always significantly evident in the corresponding surface measurements. Topographical features play a key role in determining the surface exposures from column abundance especially in a coastal city like Wollongong. The topography at Wollongong, combined with meteorological effects, potentially exacerbates differences in the column and surface. Hence, satellite column amounts are unlikely to provide an accurate reflection of exposure at the ground during major events like the 2019–2020 bushfires.

Accounting for meteorological effects in the the detector of the charged component of cosmic rays

In this paper, we discuss the influence of meteorological effects on the data of the ground installation CARPET, which is a detector of the charged component of secondary cosmic rays (CRs). This device is designed in the P.N. Lebedev Physical Institute (LPI, Moscow, Russia) and installed at the Dolgoprudny scientific station (Dolgoprudny, Moscow region, S55.56 °, W37.3 °; Rc = 2.12 GV) in 2017. Based on the data obtained in 2019–2020, the barometric and temperature coefficients for the CARPET installation were determined. The barometric coefficient was calculated from the data of the barometric pressure sensor included in the installation. To determine the temperature effect, we used the data of upper-air sounding of the atmosphere obtained by the Federal State Budgetary Institution «Central Aerological Observatory» (CAO), also located in Dolgoprudny.