Investigating the Impacts of the COVID-19 Lockdown on Trace Gases Using Ground-Based MAX-DOAS Observations in Nanjing, China

The spread of the COVID-19 pandemic and consequent lockdowns all over the world have had various impacts on atmospheric quality. This study aimed to investigate the impact of the lockdown on the air quality of Nanjing, China. The off-axis measurements from state-of-the-art remote-sensing Multi-Axis Differential Optical Absorption Spectroscope (MAX-DOAS) were used to observe the trace gases, i.e., Formaldehyde (HCHO), Nitrogen Dioxide (NO2), and Sulfur Dioxide (SO2), along with the in-situ time series of NO2, SO2 and Ozone (O3). The total dataset covers the span of five months, from 1 December 2019, to 10 May 2020, which comprises of four phases, i.e., the pre lockdown phase (1 December 2019, to 23 January 2020), Phase-1 lockdown (24 January 2020, to 26 February 2020), Phase-2 lockdown (27 February 2020, to 31 March 2020), and post lockdown (1 April 2020, to 10 May 2020). The observed results clearly showed that the concentrations of selected pollutants were lower along with improved air quality during the lockdown periods (Phase-1 and Phase-2) with only the exception of O3, which showed an increasing trend during lockdown. The study concluded that limited anthropogenic activities during the spring festival and lockdown phases improved air quality with a significant reduction of selected trace gases, i.e., NO2 59%, HCHO 38%, and SO2 33%. We also compared our results with 2019 data for available gases. Our results imply that the air pollutants concentration reduction in 2019 during Phase-2 was insignificant, which was due to the business as usual conditions after the Spring Festival (Phase-1) in 2019. In contrast, a significant contamination reduction was observed during Phase-2 in 2020 with the enforcement of a Level-II response in lockdown conditions i.e., the easing of the lockdown situation in some sectors during a specific interval of time. The observed ratio of HCHO to NO2 showed that tropospheric ozone production involved Volatile Organic Compounds (VOC) limited scenarios.This work was supported by the National Natural Science Foundation of China (NSFC, 41701551, 41605117, 41771291). Y.W. was supported by the National Science Foundation

Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns

In many areas, such as plains and gently undulating terrain, easy-to-measure soil-forming factors such as landform and vegetation do not co-vary with soil conditions across space to the level that they can be effectively used in digital soil mapping. A challenging problem is how to develop a new environmental variable that co-varies with soil spatial variation under these situations. This study examined the idea that change patterns (dynamic feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, can be used to differentiate soil types. To examine this idea, we selected two study areas with different climates: one in northeastern China and the other in northwestern China. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to capture land surface feedback. To measure feedback dynamics, we used spectral information divergence (SID). Results of an independent-samples t-test showed that there was a significant difference in SID values between pixel pairs of the same soil subgroup and those of different subgroups. This indicated that areas with different soil types (subgroup level) exhibited significantly different dynamic feedback patterns, and areas within the same soil type have similar dynamic feedback patterns. It was also found that the more similar the soil types, the more similar the feedback patterns. These findings could lead to the development of a new environmental covariate that could be used to improve the accuracy of soil snapping in low-relief areas

Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns

In many areas, such as plains and gently undulating terrain, easy-to-measure soil-firming factors such as landform and vegetation do not co-vary with soil conditions across space to the level that they can be effectively used in digital soil mapping. A challenging problem is how to develop a new environmental variable that co-varies with soil spatial variation under these situations. This study examined the idea that change patterns (dynamic feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, can be used to differentiate soil types. To examine this idea, we selected two study areas with different climates: one in northeastern China and the other in northwestern China. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to capture land surface feedback, To measure feedback dynamics, we used spectral information divergence (SID). Results of an independent-samples t-test showed that there was a significant difference in SID values between pixel pairs of the same soil subgroup and those of different subgroups. This indicated that areas with different soil types (subgroup level) exhibited significantly different dynamic feedback patterns, and areas within the same soil type have similar dynamic feedback patterns. It was also found that the more similar the soil types, the more similar the feedback patterns. These findings could lead to the development of a new environmental covariate that could be used to improve the accuracy of soil mapping in low-relief areas. © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved

Observations of the vertical distributions of summertime atmospheric pollutants and the corresponding ozone production in Shanghai, China

Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and lidar measurements were performed in Shanghai, China, during May 2016 to investigate the vertical distribution of summertime atmospheric pollutants. In this study, vertical profiles of aerosol extinction coefficient, nitrogen dioxide (NO2) and formaldehyde (HCHO) concentrations were retrieved from MAX-DOAS measurements using the Heidelberg Profile (HEIPRO) algorithm, while vertical distribution of ozone (O-3) was obtained from an ozone lidar. Sensitivity study of the MAX-DOAS aerosol profile retrieval shows that the a priori aerosol profile shape has significant influences on the aerosol profile retrieval. Aerosol profiles retrieved from MAX-DOAS measurements with Gaussian a priori profile demonstrate the best agreements with simultaneous lidar measurements and vehicle-based tethered-balloon observations among all a priori aerosol profiles. Tropospheric NO2 vertical column densities (VCDs) measured with MAX-DOAS show a good agreement with OMI satellite observations with a Pearson correlation coefficient (R) of 0.95. In addition, measurements of the O-3 vertical distribution indicate that the ozone productions do not only occur at surface level but also at higher altitudes (about 1.1 km). Planetary boundary layer (PBL) height and horizontal and vertical wind field information were integrated to discuss the ozone formation at upper altitudes. The results reveal that enhanced ozone concentrations at ground level and upper altitudes are not directly related to horizontal and vertical transportation. Similar patterns of O3 and HCHO vertical distributions were observed during this campaign, which implies that the ozone productions near the surface and at higher altitudes are mainly influenced by the abundance of volatile organic compounds (VOCs) in the lower troposphere

The Qur'an and Identity in Contemporary Chinese Fiction

How is it possible to comprehend and assess the impact of the Qur’an on the literary expressions of Chinese Muslims (Hui) when the first full ‘translations’ of the Qur’an in Chinese made by non-Muslims from Japanese and English appeared only in 1927 and 1931, and by a Muslim from Arabic in 1932? But perhaps the fact that such a translation appeared so late in the history of the Muslim community in China, who have had a continuous presence since the ninth-century, is the best starting point. For it would be possible to address the relationship between the sacred text (as well as language) and identity among minority groups in a different way. This paper looks at the ways in which the Qur’an is imagined then embodied in literary texts authored by two prize-winning Chinese Muslim authors: Huo Da (b. 1945) and Zhang Chengzhi (b. 1948). While Huo Da, who does not have access to the Arabic language, alludes to the Chinese Qur’an in her novel, The Muslim’s Funeral (1982), transforming the its teachings into ritual performances of alterity through injecting Arabic and Persian words for religious rituals into her narrative of a Muslim family’s fortunes at the turn of the twentieth century, Zhang Chengzhi, who learned Arabic as an adult and travelled widely in the Muslim world, involves himself in reconstructing the history of the spread and persecution of the Jahriyya Sufi sect (an off-shoot of the Naqshabandiyya) in China between the seventeenth and nineteenth centuries in his only historical novel, A History of the Soul (1991), and in education reform in Muslim communities, inventing an identity for Chinese Muslims based on direct knowledge of the sacred text and tradition and informed by the history of Islam not in China alone but in the global Islamic world, especially Arabic Islamic history

Delineation of support domain of feature in the presence of noise

Clustered events are usually deemed as feature when several spatial point processes are overlaid in a region. They can be perceived either as a precursor that may induce a major event to come or as offspring triggered by a major event. Hence, the detection of clustered events from point processes may help to predict a forthcoming major event or to study the process caused by a major event. Nevertheless, the locations of existing clustered events alone are not sufficient to identify the area susceptible to a potential major future event or to predict the potential locations of similar future events, so it is desirable to know the shape and the size of the region (the "territory" of feature events) that the feature process occupies. In this paper, the support domain of feature (SDF), the region over which any feature event has the equivalent likelihood to occur, is employed to approximate the "territory" of feature events. A method is developed to delineate the SDF from a region containing spatial point processes. The method consists of three major steps. The first is to construct a discrimination function for separating feature points from noise points. The second is to divide the entire area into a regular mesh of points and then compute a fuzzy membership value for each grid point belonging to the SDF. The final step is to trace the boundary of the SDF. The algorithm was applied to two seismic cases for evaluation, one is the Lingwu earthquake and the other is the Longling earthquakes. Results show that the main earthquakes in both areas as well as most aftershocks triggered by them fell into the estimated SDFs. The case study of Longling shows that the algorithm can deal with a region containing more than two processes. © 2007 Elsevier Ltd. All rights reserved

Detecting feature from spatial point processes using collective nearest-neighbor

a b s t r a c t In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to identify features. CLNN assumes that in spatial data clustered points and noise can be viewed as two homogenous point processes. The one with the higher intensity is considered as a feature and the one with the lower intensity is treated as noise. As a result, they can be separated according to the difference in intensity between them. With CLNN, points are first classified into feature and noise based on the kth nearest distance (the distance between a point and its kth nearest neighbor) at various values of k. Then, CLNN selects those classifications in which the separated classes (i.e. features and noise) are homogenous Poisson processes and cannot be further divided. Finally, CLNN identifies clustered points by averaging the selected classifications. Evaluation of CLNN using simulated data shows that CLNN reduces the number of false points significantly. The comparison between CLNN, the shared nearest neighbor, the spatial scan and the classification entropy method shows that CLNN produced the fewest false points. A case study using seismic data in southwestern China showed that CLNN is able to identify foreshocks of the Songpan earthquake (M = 7.2), which may help to locate the epicenter of the Songpan earthquake

Investigating the Effect of Different Meteorological Conditions on MAX-DOAS Observations of NO2 and CHOCHO in Hefei, China

In this work, a ground-based remote sensing instrument was used for observation of the trace gases NO2 and CHOCHO in Hefei, China. Excessive development and rapid economic growth over the years have resulted in the compromising of air quality in this city, with haze being the most prominent environmental problem. This is first study covering observation of CHOCHO in Hefei (31.783° N, 117.201° E). The observation period of this study, i.e., July 2018 to December 2018, is divided into three different categories: (1) clear days, (2) haze days, and (3) severe haze days. The quality of the differential optical absorption spectroscopy (DOAS) fit for both CHOCHO and NO2 was low during severe haze days due to a reduced signal to noise ratio. NO2 and CHOCHO showed positive correlations with PM2.5, producing R values of 0.95 and 0.98, respectively. NO2 showed strong negative correlations with visibility and air temperature, obtaining R values of 0.97 and 0.98, respectively. CHOCHO also exhibited strong negative correlations with temperature and visibility, displaying R values of 0.83 and 0.91, respectively. The average concentration of NO2, CHOCHO, and PM2.5 during haze days was larger compared to that of clear days. Diurnal variation of both CHOCHO and NO2 showed a significant decreasing trend in the afternoons during clear days due to photolysis, while during haze days these two gases started to accumulate as their residence time increases in the absence of photolysis. There was no prominent weekly cycle for both trace gases