Dialect connectedness and tunneling: evidence from China

PurposeThis study aims to examine the effects of dialect connectedness between the chairman and the chief executive officer (CEO) (DCCC) on the tunneling activities of controlling shareholders.Design/methodology/approachThis study uses abnormal related-party transactions (ARPT) as a proxy for tunneling activities and traces dialects of chairmen and CEOs based on the respective birthplace information. Baseline results are examined using a fixed-effects model. The results remain robust when using the instrumental variable approach, propensity score matching (PSM) technique, changing the measurement of tunneling and Heckman two-step selection model.FindingsThe results show that DCCC reduces tunneling activities. This negative association is more pronounced for non-state-owned enterprises and firms whose chairmen and CEOs work in the respective hometowns. DCCC restrains tunneling activities through mechanisms by establishing an informal supervisory effect on CEOs because the CEOs fear reputational damage and strengthening cooperation between chairmen and CEOs. Further analyses suggest that this negative association is more significant when chairmen and CEOs are non-controlling shareholders, but the association is weakened during the coronavirus disease 2019 (COVID-19) crisis.Originality/valueAs dialect is a carrier of culture, this study's results imply that cultural proximity can replace formal mechanisms to enhance corporate governance.

Differences in infant negative affectivity during the COVID-19 pandemic.

This longitudinal study compared infant temperament rated at 3 months postpartum by 263 United-States-based women who gave birth during the COVID-19 pandemic and 72 who gave birth prior to the pandemic. All women completed questionnaires assessing perinatal mental health, social contact, and infant temperament. Mothers whose infants were born during the pandemic reported higher levels of infant negative affectivity as compared with mothers whose infants were born earlier (F(1, 324) = 18.28, p < .001), but did not differ in their ratings of surgency or effortful control. Maternal prenatal depressive symptoms, prenatal stress, and postpartum stress mediated differences in infant negative affectivity  between pandemic and pre-pandemic groups. Within the pandemic group, decreased postpartum social contact was associated with higher ratings of infant negative affectivity. These findings suggest that the pandemic has affected maternal perceptions of infant temperament, perinatal mental health, and social contact.

Este estudio longitudinal comparó el temperamento del infante evaluado a los tres meses después del parto por 263 mujeres con base en Estados Unidos, las cuales dieron a luz durante la pandemia del COVID-19 y 72 que dieron a luz antes de la pandemia. Todas las mujeres completaron cuestionarios para evaluar la salud mental perinatal, el contacto social y el temperamento del infante. Las madres cuyos infantes nacieron durante la pandemia reportaron más altos niveles de afectividad negativa del infante tal como se les comparó con madres cuyos infantes nacieron antes (F(1,324) = 18.28, p<.001), pero no difirieron en sus puntajes de rapidez y astucia o control esforzado. Los síntomas depresivos maternos mediaron la asociación entre la condición de pandemia y la afectividad negativa del infante. Dentro del grupo de pandemia, la baja en el contacto social posterior al parto fue asociada con más altos puntajes en la afectividad negativa del infante. Estos resultados proponen que la pandemia ha afectado las percepciones mentales de la salud mental y el contacto social del temperamento perinatal del infante.

Cette étude longitudinale a comparé le tempérament du nourrisson évalué à trois mois postpartum par 263 femmes basées aux Etats-Unis d'Amérique ayant donné naissance durant la pandémie du COVD-19 et 72 femmes ayant donné naissance avant la pandémie. Toutes les femmes ont rempli des questionnaires évaluant la santé mentale périnatale, le contact social et le tempérament du nourrisson. Les mères dont les nourrissons étaient nés durant la pandémie ont fait état de niveaux plus élevés d'affectivité négative du bébé comparées aux mères dont les bébés étaient nés avant (F(1 324) = 18,28, p <,001), mais n'ont pas divergé dans leurs évaluations du dynamisme ou du contrôle efficace. Les symptômes dépressifs maternels ont médiatisé le lien entre le statue pandémique et l'affectivité négative du nourrisson. Au sein du groupe pandémique le contact social postpartum décru était lié à des évaluations plus élevées de l'affectivité négative du nourrisson. Ces résultats suggèrent que la pandémie a affecté les perceptions maternelles du tempérament du bébé, la santé mentale périnatale et le contact social.

The SARS-CoV-2 Nsp13 Helicase Required for Coronavirus Replication Interacts with Structured Nucleic Acids in a Strand-Specific Manner and Catalyzes a Novel Protein-RNA Remodeling Activity Implicated in the Proofreading Mechanism of the Replication-Transc

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded, positive-sense RNA virus responsible for COVID-19, requires a set of virally encoded nonstructural proteins that compose a replication-transcription complex (RTC) to replicate its 30 kilobase genome. One such nonstructural protein within the RTC is Nsp13, a highly conserved molecular motor ATPase/helicase. Upon purification of the recombinant SARS-CoV-2 Nsp13 protein expressed using a eukaryotic cell-based system, we biochemically characterized the enzyme by examining its catalytic functions, nucleic acid substrate specificity, and putative protein-nucleic acid remodeling activity. We determined that Nsp13 preferentially interacts with single-stranded (ss) DNA compared to ssRNA during loading to unwind with greater efficiency a partial duplex helicase substrate. The binding affinity of Nsp13 to nucleic acid was confirmed through electrophoretic mobility shift assays (EMSA) by determining that Nsp13 binds to DNA substrates with significantly greater efficiency than RNA. These results demonstrate strand-specific interactions of SARS-CoV-2 Nsp13 that dictate its ability to load and unwind structured nucleic acid substrates. We next determined that Nsp13 catalyzed unwinding of double-stranded (ds) RNA forked duplexes on substrates containing a backbone disruption (neutrally charged polyglycol linker (PGL)) was strongly inhibited when the PGL was positioned in the 5' ssRNA overhang, suggesting an unwinding mechanism in which Nsp13 is strictly sensitive to perturbation of the translocating strand sugar-phosphate backbone integrity. Furthermore, we demonstrated for the first time the ability of the coronavirus Nsp13 helicase to disrupt a high-affinity nucleic acid-protein interaction, i.e., a streptavidin tetramer bound to biotinylated RNA or DNA substrate, in a uni-directional manner and with a preferential displacement of the streptavidin complex from biotinylated ssDNA versus ssRNA. In contrast to the poorly hydrolysable ATP-gamma-S or non-hydrolysable AMP-PNP, ATP supports Nsp13-catalyzed disruption of the nucleic acidprotein complex, suggesting that nucleotide binding by Nsp13 is not sufficient for protein-RNA disruption and the chemical energy of nucleoside triphosphate hydrolysis is required to fuel remodeling of protein bound to RNA or DNA. Our results build upon structural studies of the SARS-CoV-2 RTC in which it was suggested that Nsp13 pushes the RNA polymerase (Nsp12) backward on the template RNA strand. Experimental evidence from our studies demonstrate that Nsp13 helicase efficiently remodels a large high affinity protein-RNA complex in a manner dependent on its intrinsic ATP hydrolysis function. We proposed that this novel biochemical activity of Nsp13 is relevant to its role in SARS-CoV-2 RNA processing functions and replication. It was proposed that Nsp13 facilitates proofreading during coronavirus replication when a mismatched base is inadvertently incorporated into the SARS-CoV-2 genome during replication to reposition the RTC so that the proofreading nuclease complex (Nsp14-Nsp10) can gain access and remove the nascently synthesized nucleotide to ensure polymerase fidelity. Our findings implicate a direct catalytic role of Nsp13 in protein-RNA remodeling during coronavirus genome replication beyond its duplex strand separation or structural stabilization of the RTC, yielding new insight into the proofreading mechanism. This work was supported by the Intramural Training Program, National Institute on Aging (NIA), NIH, and a Special COVID-19 Grant from the Office of the Scientific Director, NIA, NIH.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.

Spatial variability of trace gases (NO2, O3 and CO) over Indian region during 2020 and 2021 COVID-19 lockdowns.

COVID-19 lockdown has given us an opportunity to investigate the pollutant concentrations in response to the restricted anthropogenic activities. The atmospheric concentration levels of nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) have been analysed for the periods during the first wave of COVID-19 lockdown in 2020 (25th March-31st May 2020) and during the partial lockdowns due to second wave in 2021 (25th March-15th June 2021) across India. The trace gas measurements from Ozone Monitoring Instrument (OMI) and Atmosphere InfraRed Sounder (AIRS) satellites have been used. An overall decrease in the concentration of O3 (5-10%) and NO2 (20-40%) have been observed during the 2020 lockdown when compared with business as usual (BAU) period in 2019, 2018 and 2017. However, the CO concentration increased up to 10-25% especially in the central-west region. O3 and NO2 slightly increased or had no change in 2021 lockdown when compared with the BAU period, but CO showed a mixed variation prominently influenced by the biomass burning/forest fire activities. The changes in trace gas levels during 2020 lockdown have been predominantly due to the reduction in the anthropogenic activities, whereas in 2021, the changes have been mostly due to natural factors like meteorology and long-range transport, as the emission levels have been similar to that of BAU. Later phases of 2021 lockdown saw the dominant effect of rainfall events resulting in washout of pollutants. This study reveals that partial or local lockdowns have very less impact on reducing pollution levels on a regional scale as natural factors like atmospheric long-range transport and meteorology play deciding roles on their concentration levels.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis.

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Early-phase pandemic in Italy: Covid-19 spread determinant factors.

Although the Covid-19 pandemic is still ongoing, the environmental factors beyond virus transmission are only partially known. This statistical study has the aim to identify the key factors that have affected the virus spread during the early phase of pandemic in Italy, among a wide set of potential determinants concerning demographics, environmental pollution and climate. Because of its heterogeneity in pollution levels and climate conditions, Italy provides an ideal scenario for an ecological study. Moreover, the selected period excludes important confounding factors, as different virus variants, restriction policies or vaccines. The short-term relationship between the infection maximum increase and demographic, pollution and meteo-climatic parameters was investigated, including both winter-spring and summer 2020 data, also focusing separately on the two seasonal periods and on North vs Centre-South. Among main results, the importance of population size confirmed social distancing as a key management option. The pollution hazardous role undoubtedly emerged, as NO2 affected infection increase in all the studied scenarios, PM2.5 manifested its impact in North of Italy, while O3 always showed a protective action. Whereas higher temperatures were beneficial, especially in the cold season with also wind and relative humidity, solar irradiance was always relevant, revealing several significant interactions with other co-factors. Presented findings address the importance of the environment in Sars-CoV-2 spread and indicated that special carefulness should be taken in crowded areas, especially if they are highly polluted and weakly exposed to sun. The results suggest that containment of future epidemics similar to Covid-19 could be supported by reducing environmental pollution, achieving safer social habits and promoting preventive health care for better immune system response, as an only comprehensive strategy.

A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region.

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.

Changes in NO2 and O3 levels due to the pandemic lockdown in the industrial cities of Tehran and Arak, Iran using Sentinel 5P images, Google Earth Engine (GEE) and statistical analysis.

Air pollution has very damaging effects on human health. In recent years the Coronavirus disease (COVID-19) pandemic has created a worldwide economic disaster. Although the consequences of the COVID-19 lockdowns have had severe effects on economic and social conditions, these lockdowns also have also left beneficial effects on improving air quality and the environment. This research investigated the impact of the COVID-19 lockdown on NO2 and O3 pollutants changes in the industrial and polluted cities of Arak and Tehran in Iran. Based on this, the changes in NO2 and O3 levels during the 2020 lockdown and the same period in 2019 were investigated in these two cities. For this purpose, the Sentinel-5P data of these two pollutants were used during the lockdown period from November 19 to December 05, 2020, and at the same time before the pandemic from November 19 to December 05, 2019. For better results, the effect of climatic factors such as rain and wind in reducing pollution was removed. The obtained results indicate a decrease in NO2 and O3 levels by 3.5% and 6.8% respectively in Tehran and 20.97% and 5.67% in Arak during the lockdown of 2020 compared to the same time in 2019. This decrease can be caused by the reduction in transportation and socio-economic and industrial activities following the lockdown measures. This issue can be a solid point to take a step toward controlling and reducing pollution in non-epidemic conditions by implementing similar standards and policies in the future.

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD.

The air pollution in China currently is characterized by high fine particulate matter (PM2.5) and ozone (O3) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM2.5 decreased while O3 increased in most cities due to the effect of COVID-19, and the increase in O3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM2.5-O3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM2.5-O3 VM-DCCA exponents [Formula: see text] in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its [Formula: see text] is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM2.5 and O3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM2.5-O3 DHP coordinated control strategies.

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future. © 2023 by the authors.

Footprints of COVID-19 on Pollution in Southern Spain

Background: Many annual deaths in Spain could be avoided if pollution levels were reduced. Every year, several municipalities in the Community of Andalusia, located in southern Spain, exceed the acceptable levels of atmospheric pollution. In this sense, the evolution of primary air pollutants during the March–June 2020 lockdown can be taken as reliable evidence to analyze the effectiveness of potential air quality regulations. Data and Method: Using a multivariate linear regression model, this paper assesses the levels of NO2, O3, and PM10 in Andalusia within the 2017–2020 period, relating these representative indices of air quality with lockdown stages during the pandemic and considering control variables such as climatology, weekends, or the intrusion of Saharan dust. To reveal patterns at a local level between geographic zones, a spatial analysis was performed. Results: The results show that the COVID-19 lockdown had a heterogeneous effect on the analyzed pollutants within Andalusia's geographical regions. In general terms, NO2 and PM10 concentrations decreased in the main metropolitan areas and the industrial districts of Huelva and the Strait of Gibraltar. At the same time, O3 levels rose in high-temperature regions of Cordoba and Malaga. © 2022 by the authors.

Two-Level 3D Column-like Nanofilms with Hexagonally-Packed Tantalum Fabricated via Anodizing of Al/Nb and Al/Ta Layers-A Potential Nano-Optical Biosensor.

Reanodizing metal underlayers through porous anodic alumina has already been used extensively to fabricate ordered columns of different metal oxides. Here, we present similar 3D multilayered nanostructures with unprecedented complexity. Two-level 3D column-like nanofilms have been synthesized by anodizing an Al/Nb metal layer in aqueous oxalic acid for forming the first level, and an Al/Ta layer in aqueous tartaric acid for forming the second level of the structure. Both levels were then reanodized in aqueous boric acid. The Ta layer deposited on partially dissolved porous anodic alumina of the first level, with protruding tops of niobia columns, acquired a unique hexagonally-packed structure. The morphology of the first and second levels was determined using scanning electron microscopy. Prolonged etching for 24 h in a 50%wt aqueous phosphoric acid was used to remove the porous anodic alumina. The formation mechanism of aluminum phosphates on the second-level columns in the process of long-time cold etching is considered. The model for the growth of columns on a Ta hexagonally-packed structure of the second level is proposed and described. The described approach can be applied to create 3D two- or three-level column-like systems from various valve metals (Ta, Nb, W, Hf, V, Ti), their combinations and alloys, with adjustable column sizes and scaling. The results of optical simulation show a high sensitivity of two-level column-like 3D nanofilms to biomedical objects and liquids. Among potential applications of these two-level column-like 3D nanofilms are photonic crystals for full-color displays, chemical sensors and biosensor, solar cells and thermoresponsive shape memory polymers.

Important revelations of different degrees of COVID-19 lockdown on improving regional air quality: a case study of Shijiazhuang, China.

To control the spread of COVID-19, Shijiazhuang implemented two lockdowns of different magnitudes in 2020 (lockdown I) and 2021 (lockdown II). We analyzed the changes in air quality index (AQI), PM2.5, O3, and VOCs during the two lockdowns and the same period in 2019 and quantified the effects of anthropogenic sources during the lockdowns. The results show that AQI decreased by 13.2% and 32.4%, and PM2.5 concentrations decreased by 12.9% and 42.4% during lockdown I and lockdown II, respectively, due to the decrease in urban traffic mobility and industrial activity levels. However, the sudden and unreasonable emission reductions led to an increase in O3 concentrations by 160.6% and 108.4%, respectively, during the lockdown period. To explore the causes of the O3 surge, the major precursors NOx and VOCs were studied separately, and the main VOCs species affecting ozone formation during the lockdown period and the source variation of VOCs were identified, and it is important to note that the relationship between diurnal variation characteristics of VOCs and cooking became apparent during the lockdown period. These findings suggest that regional air quality can be improved by limiting production, but attention should be paid to the surge of O3 caused by unreasonable emission reductions, clarifying the control priorities for urban O3 management.

[Numerical Response Analysis of PM2.5-O3 Compound Pollution in Beijing].

The multi-scale variation trend of PM2.5-O3 compound pollution events was analyzed based on air quality data, meteorological data, and COVID-19 data in Beijing from 2015 to 2020. For the threshold of compound pollution, a compound pollution index was proposed, and the numerical response trend was evaluated based on the generalized additive model. A distributed lag nonlinear model was introduced to analyze the risk response relationship between compound pollution and influencing factors. The results showed that the events of PM2.5-O3 compound pollution in Beijing decreased annually. At the same time, due to the influence of pollutant emissions and meteorological conditions, there were obvious seasonal effects, week effects, holiday effects, and epidemic effects. The composite pollution index had no correlation with rainfall but had a linear positive correlation with O3 and air temperature and a nonlinear correlation with other explanatory variables. Air pollutants and meteorological conditions had obvious lag effects on the composite pollution index, and the lag effects were mainly concentrated in 1-3 d. PM2.5, PM10, O3, SO2, and air temperature in high-value areas significantly increased the risk of compound pollution. The CO (1-6 mg·m-3), NO2 (38-118 µg·m-3), and relative humidity (54%-87%) in the median section would also increase the risk of compound pollution, as would low wind speed. The compound pollution events showed a trend of multi-day continuous pollution in the numerical response. Compared with PM2.5 and PM10, compound pollution events were more dependent on O3, and the compound pollution rate in high-value areas was 30.7%-47.5%. CO and relative humidity had little effect on compound pollution events. The air temperature had the greatest impact, and 84.7% of the composite pollution incidents occurred at 20-30℃.

On the transition of major pollutant and O3 production regime during Covid-19 lockdowns.

Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O3 under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O3 and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O3, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 µgm-3; O3 = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO2 (-40%), CO (-37%) while O3 remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 µgm-3; O3 = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO2 (-4.7%), CO (-49%) while O3 increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O3 is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O3 varies with the ratio, NO/NO2, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O3 isopleths as a function of NOx and VOC depicting distinct patterns suggest that O3 variation is entirely non-linear for a given NOx or VOC.

A retrospect of ozone formation mechanisms during the COVID-19 lockdown: The potential role of isoprene.

Wuhan took strict measures to prevent the spread of COVID-19 from January 26 to April 7 in 2020. The lockdown reduced the concentrations of atmospheric pollutants, except ozone (O3). To investigate the increase in O3 during the lockdown, trace gas pollutants were collected. The initial concentrations of volatile organic compounds (VOCs) were calculated based on a photochemical ratio method, and the ozone formation potential (OFP) was obtained using the initial and measured VOC concentrations. The O3 formation regime was NOX-limited based on the VOCs/NOX diurnal ratios during the lockdown period. The reduced nitric oxide (NO) concentrations and lower wind speed (WS) could explain the night-time O3 accumulation. The initial total VOCs (TVOCs) during the lockdown were 47.6 ± 2.9 ppbv, and alkenes contributed 48.1%. The photochemical loss amounts of alkenes were an order of magnitude higher than those of alkenes in the same period in 2019 and increased from 16.6 to 28.0 ppbv in the daytime. The higher initial alkene concentrations sustained higher OFP during the lockdown, reaching between 252.4 and 504.4 ppbv. The initial isoprene contributed approximately 35.0-55.0% to the total OFP and had a positive correlation with the increasing O3 concentrations. Approximately 75.5% of the temperatures were concentrated in the range of 5 and 20 °C, which were higher than those in 2019. In addition to stronger solar radiation, the higher temperatures induced higher isoprene emission rates, partially accounting for the higher isoprene concentrations. Lower isoprene-emitting trees should be considered for future urban vegetation to control O3 episodes.

Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning.

Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 µg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.

Ambient PM2.5 and O3 pollution and health impacts in Iranian megacity.

The main objectives of this study were to (i) assess variation within fine particles (PM2.5) and tropospheric ozone (O3) time series in Khorramabad (Iran) between 2019 (before) and 2020 (during COVID-19 pandemic); (ii) assess relationship between PM2.5 and O3, the PM2.5/O3 ratio, and energy consumption; and (iii) estimate the health effects of exposure to ambient PM2.5 and O3. From hourly PM2.5 and O3 concentrations, we applied both linear-log and integrated exposure-response functions, city-specific relative risk, and baseline incidence values to estimate the health effects over time. A significant correlation was found between PM2.5 and O3 (r =-0.46 in 2019, r =-0.55 in 2020, p < 0.05). The number of premature deaths for all non-accidental causes (27.5 and 24.6), ischemic heart disease (7.3 and 6.3), chronic obstructive pulmonary disease (17 and 19.2), and lung cancer (9.2 and 6.25) attributed to ambient PM2.5 exposure and for respiratory diseases (4.7 and 5.4) for exposure to O3 above 10 µg m-3 for people older than 30-year-old were obtained in 2019 and 2020. The number of years of life lost declined by 11.6% in 2020 and exposure to PM2.5 reduced the life expectancy by 0.58 and 0.45 years, respectively in 2019 and 2020. Compared to 2019, the restrictive measures associated to COVID-19 pandemic led to reduction in PM2.5 (-25.5%) and an increase of O3 concentration (+ 8.0%) in Khorramabad.

Preparation of LiFePO4/C cathode material by extracting Fe2O3 from laterite nickel ore by ammonium jarosite method

In the context of the COVID-19 pandemic and the war between Russia and Ukraine, domestic oil prices have skyrocketed. Saving resources and develop lithium ion batteries with excellent performance are particularly important. In view of unreasonable utilization of non-target elements in laterite nickel ore and high energy consumption of traditional sulfate roasting laterite nickel ore, a pioneering idea was adopted to combine laterite nickel ore with LiFePO 4 , which not only meets the resource saving but also prepares lithium ion batteries with excellent performance, in this study. Using ammonium sulfate roasting laterite nickel ore-ammonium jarosite iron precipitation and hydrolysis preparation of Fe 2 O 3 -carbon thermal reduction preparation of LiFePO 4 /C process means, to achieve the ultimate goal of preparing LiFePO 4 from laterite nickel ore. Determining the optimum conditions of each part of experiment by single factor experiment and orthogonal experiment. It was concluded that under the optimal preparation conditions, the discharge specific capacity of lithium ion battery was 164.56 mAh/g at the rate of 0.5 C, and it was 94% of the theoretical capacity. After 100 cycles, we could find that the discharge specific capacity could be maintained at 162.78 mAh/g, and the capacity retention rate still reached 98%. • Preparation of LiFePO 4 /C cathode material from laterite nickel ore for rational utilization of resources. • Ammonium sulfate roasting laterite nickel ore can greatly improve the recovery rate of nickel and iron resources • The ammonium jarosite method has high iron precipitation rate, simple operation and less pollution. • The preparation of LiFePO 4 /C cathode material by carbothermal reduction method is conducive to practical applications. [ FROM AUTHOR]

Governing digital crisis responses: platform standards and the dilemma of COVID-19 contact tracing

In response to the impact of the SARS-CoV-2 (COVID-19) pandemic, various developers turned to smartphone-based contact tracing to address the challenges of manual tracing. Due to the presence of network effects, i.e., the effectiveness of contact tracing applications increases with the number of users, information technology standards were critical to the technology's success. The standardization efforts in Europe led to a variety of trade-offs concerning the choice of an appropriate technological architecture due to the contradictory tensions resulting from the dualism between the need for contact tracing data to contain the pandemic and the need for data minimization to preserve user privacy. Drawing predominantly on the software platform and standards literature, we conduct an interpretive case study to examine the emergence and consequences of this multi-layered decision situation. Our findings reveal how Google and Apple were able to limit the individual leeway of external developers, thereby effectively resolving the European standards war. Furthermore, we identify and discuss the various short-term and long-term trade-offs associated with the standardization of contact tracing applications and translate our findings into recommendations for policy makers with respect to future crisis situations. Specifically, we propose a strategy grounded in our data that enables responsible actors to make goal-oriented and rapid decisions under time constraints.