Review on Atmospheric Ozone Pollution in China: Formation, Spatiotemporal Distribution, Precursors and Affecting Factors

In recent years, atmospheric ozone pollution has become more and more serious in many areas of China due to the rapid development of industrialization and urbanization. The increase in atmospheric ozone concentration will not only cause harm to the human respiratory tract, nervous system and immune system, but also cause obvious harm to crops, which will lead to reductions in crop production. Therefore, the study of atmospheric ozone pollution should not be ignored in research on the atmospheric environment. In this paper, we summarized the formation mechanisms of atmospheric ozone, the spatiotemporal distribution characteristics of atmospheric ozone in some areas of China, the relationship between atmospheric ozone and its precursors, and the main factors affecting the concentration of atmospheric ozone. Then, the control countermeasures against atmospheric ozone pollution were put forward in combination with the actual situation in China.

Experimental OMPS Radiance Assimilation through One-Dimensional Variational Analysis for Total Column Ozone in the Atmosphere

This experiment is the first ultraviolet radiance assimilation for atmospheric ozone in the troposphere and stratosphere. The experiment has provided better understanding of which observations need to be assimilated, what bias correction scheme may be optimal, and how to obtain surface reflectance. A key element is the extension of the Community Radiative Transfer Model (CRTM) to handle fully polarized radiances, which presents challenges in terms of computational resource requirements. In this study, a scalar (unpolarized) treatment of radiances was used. The surface reflectance plays an important role in assimilating the nadir mapper (NM) radiance of the Ozone Mapping and Profiler Suite (OMPS). Most OMPS NM measurements are affected by the surface reflection of solar radiation. We propose a linear spectral reflectance model that can be determined inline by fitting two OMPS NM channel radiances at 347.6 and 371.8 nm because the two channels have near zero sensitivity on atmospheric ozone. Assimilating a transformed reflectance measurement variable, the N value can overcome the difficulty in handling the large dynamic range of radiance and normalized radiance across the spectrum of the OMPS NM. It was found that the error in bias correction, surface reflectance, and neglecting polarization in radiative transfer calculations can be largely mitigated by using the two estimated surface reflectance. This study serves as a preliminary demonstration of direct ultraviolet radiance assimilation for total column ozone in the atmosphere.

sensitivity of middle atmospheric ozone to solar proton events: comparison between climate model and satellites

<p>Energetic particle precipitation (EPP) impact on the middle atmospheric ozone chemistry plays potentially an important role in the connection between space weather and Earth's climate system. A variant of the Whole Atmosphere Community Climate Model (WACCM-D) implements a detailed set of ionospheric D-region chemistry instead of a simple parameterization used in the earlier WACCM versions, allowing to capture the impact of EPP in more detail, thus improving the model for long-term climate studies. Here, we verify experimentally the ion chemistry of the WACCM-D by analysing the middle atmospheric ozone response to the EPP forcing during well-known solar proton events<span> </span>(SPEs). We use a multi-satellite approach to derive the middle atmospheric sensitivity for the SPE forcing as a statistical relation between the solar proton flux and the consequent ozone change. An identical sensitivity analysis is carried out for the WACCM-D model results, enabling one-to-one comparison with the results derived from the satellite observations. Our results show a good agreement in the sensitivity between satellites and the WACCM-D for nighttime conditions. For daytime conditions, we find a good agreement for the satellite data sets that include the largest SPEs (max proton flux >10^<span>4 </span> pfu). However, for those satellite data-sets with only minor and moderate SPEs, WACCM-D tends to underestimate the sensitivity in daytime conditions. In summary, the comparisons WACCM-D ion chemistry, combined with the transportation, demonstrates a realistic representation of the SPE sensitivity of ozone, and thus provides a conservative platform for long-term EPP impact studies.</p>

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.