Research on the 6XS6 of the SARS-CoV-2 Spike Protein

The 6XS6 is the structure of the SARS-CoV-2 spike protein. The physiological role of the spike protein is relative to the respiratory syndrome coronavirus and has a stronger infect on the human body than the ancestor virus. The purification of the 6XS6 is in the homo sapiens cell by the affinity chromatography, PBS supplemented and Size Exclusion chromatography. At last, using the Cryo-Electron Microscopy to see the structure. This paper is using the D614G mutation to illustrate the structure of the 6XS6. The N-terminal domain and C-terminal domain of the 6XS6 protein are ALA27 and VAL1137. Furthermore, the mutation doesn't have the hydrogen bond because the Asp614 is substituted by the Gly614, and the molecule that interacts with the Ala 647 may occur. While the 6XS6 structure has lots of non-covalent and disulfide bonds. Comparing the structure of the 6XS6 and 6VXX, both are glycoproteins, have three monomers, have two subunits, and have the same category of expression and classification. The different conformations of the two structures can affect the binding ability with the ACE2. This paper can help the researchers to further understand the structure and function of the 6XS6 which can be used in future experiments. © 2023 SPIE.

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

Air quality changes in Shijiazhuang during COVID-19 outbreak

[Background] The coronavirus disease 2019 (COVID-19) was first detected in December 2019. To combat the disease, a series of strict measures were adopted across the country, which led of improved air quality. This provides an opportunity to discuss the impact of human activities on air quality. [Objective] This study investigates the air quality changes in Shijiazhuang, and analyzes the impacts of epidemic prevention and control measures on air quality, so as to provide reference and ideas for further improving air quality and prevention and control measures. [Methods] The air quality data were collected online from https://www.zq12369.com/ and https://aqicn.org/city/shijiazhuang/cn/. Comparisons in air quality index (AQI) and the concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) were made between the period from December 2019 to June 2020 (reference) and the same period from 2016 to 2019 by t-test and chi-square test. [Results] The daily average AQI dropped by 25.38% in Shijiazhuang during the COVID-19 prevention and control compared with the some period from 2016 to 2019 (t=6.28, P < 0.05). The proportions of pollution days during the COVID-19 outbreak in Shijiazhuang were PM2.5 (44.56%), O3 (31.09%), PM10 (23.83%), and NO2 (2.59%) successively, the pollution days of PM10 decreased significantly (chi2=3.86, P < 0.05) compared with 2016-2019, but during traffic lockdown the numbers of pollution days of PM2.5 and in the mid stage of prevention the number of pollution days of O3 increased (P < 0.05). Compared with the control period, the concentrations of the six air pollutants decreased to varying degrees (P < 0.05), especially SO2 dropped by 55.36%. [Conclusion] The measures taken for COVID-19 control and prevention have reduced the pollution sources and emissions, which resulted in better general air quality of Shijiazhuang City, but have aggravated the pollution of O3 and other pollutants. It is necessary to further explore the causes for the aggravation of O3 pollution in order to formulate reasonable air quality control strategies.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Coupling Tracer Derived ACH with Risk Calculators to Calculate Risk of COVID-19 Infection

Risk calculators have been utilised to predict the risk of infection from SARS-CoV-2. Inputs include the dimensions of the indoor space, number of infected persons and activity, and inhalation rate of susceptible persons. The compartment model requires an estimate of the Air Changes per Hour (ACH) in the space, as the concentration is changing as a result of the dynamic balance between the generation and removal of exhaled quanta. ACH can be estimated using CO2, engineering drawings, or airflow measurements, but these estimates are often incorrect due to mechanical anomalies and mixing inefficiencies, or in the case of CO2, an absence of continuous occupancy for a sufficient amount of time. SF6 as a tracer gas to establish ACH has been used extensively for many decades to measure air exchange. This approach was utilised to assist a school in managing risk of infection in their facility during an exam period. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Growing usage of future fuels to transform the marine lubricants industry

The shipping industry has faced two major challenges during 2019-2022: the implementation of International Maritime Organization (IMO) norms on the maximum allowed sulfur concentration to be 0.5% in marine fuel, which also are known as IMO 2020 norms, from Jan 1, 2020, and the COVID-19 pandemic, which disrupted normal operations. The next challenges that the shipping industry is expected to face are the decarbonization 2030 and 2050 norms. This article will focus on the aftermaths of the IMO 2020 norms and the COVID-19 pandemic on the marine lubricants industry and how it is likely to evolve in the future as the shipping industry tries to meet the decarbonization targets.

Impacts of environmental policy uncertainty on urban environmental pollutant emissions in China: a study based on textual analysis

How to accelerate the clean use of fossil energy and promote the transformation and upgrading of energy structure is an important challenge commonly faced by countries around the world. In the post-Covid-19 era, the uncertainties faced by countries around the world are increasing and the frequency of policy adjustments in various countries is accelerating. The discharge of pollution by enterprises is significantly impacted by environmental regulatory policies. Under the carbon neutrality goal, the uncertainty of environmental policy caused by multiple political factors can directly influence the decisions made by businesses and residents, in turn, affect their confidence and expectations. However, researchers have given limited attention to measuring the environmental policy uncertainty index (EPUI). In this paper, we select 460 newspapers from the China National Knowledge Infrastructure (CNKI) newspaper database from 2001 to 2016 and use the text analysis method to directly construct China's national, provincial, and prefecture-level EPUI. The results show that China's EPUI has obvious stage characteristics and regional characteristics. By applying the Chinese city-level EPUI to the field of urban pollution reduction, we have obtained an important finding that when urban environmental policy uncertainty increases by 1%, urban industrial sulfur dioxide emission decreases by about 0.145%, and carbon dioxide emission decreases by about 0.053%. We believe that this is due to an increase in environmental policy uncertainty inhibiting the development and scaling of secondary industries.

Impact of Covid-19 Pandemic on Atmospheric Environment over China: A Satellite Perspective

The Coronavirus Disease 2019 (COVID-19) pandemic, which has lasted for more than two years, has had a huge impact on human health and the global economy, as well as the ecological environment. In this study, the variations of atmospheric environment over China from 2019 to 2020 were calculated and analyzed based on the measured total columns of ozone (O-3), sulfur dioxide (SO2), nitrogen dioxide (NO2) and aerosol optical depth (AOD) from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. The study shows the impact of the epidemic prevention and control measures and the resumption of work and production on atmospheric environment, and demonstrates that satellite remote sensing can play an important role in the monitoring of the COVID-19 pandemic, especially its impact on atmospheric environment.

Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m-3 in the North China Plain (NCP) and 23 µg m-3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.

Does environmental quality and weather induce COVID-19: Case study of Istanbul, Turkey

The coronavirus pandemic has infected more than 100 million people worldwide with COVID-19, with millions of deaths across the globe. In this research, we explored the effects of environmental and weather variables with daily COVID-19 cases and COVID-19 fatalities in Istanbul, Turkey. Turkey has the 8th highest number of COVID-19 cases globally, with the highest infections and deaths in Istanbul. This may be the first study to conduct a comprehensive investigation for environmental quality (air quality pollutants, e.g., PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, etc.), weather parameters (temperature, humidity) and COVID-19 in Turkey. The authors collected meteorological data from 11 March 2020 to 8 February 2021 and COVID-19 data from Istanbul and other regions. The results from empirical estimations, correlation analysis, and quantile on quantile techniques support that air quality and temperature significantly influence COVID-19 deaths in Istanbul. This research may help policymakers and health scientists to take specific measures to reduce the spread of coronavirus across different global cities.The effects of air quality on COVID-19 in Istanbul was investigated.The study applied correlation and quantile on quantile techniques over daily data.Temperature significantly induces the spread of COVID-19 in Istanbul at all quantiles.Air quality and Nitrogen are positively linked with COVID-19 new cases.

Role of the COVID-19 imposed lockdown in climate change

As the transmission of COVID-19 increases rapidly, the whole world adopted the lockdown activity with restriction of human mobility to prevent its spread. Everyone thinks of the COVID-19 negatively;however, it has some positive aspects too. Before COVID-19, the world was suffering by a high level of urban air pollution especially in the form of CO2, SO2, NO2 and particulate matter. During the COVID-19 pandemic, lockdown and limited human engagement with nature accompanied by social distance have proven to be beneficial for nature. As a result, significant reduction in environmental pollution and improvement in the quality of air, cleaner rivers, less noise pollution, undisturbed and calm wildlife was observed. Knowledge gained from the studies suggests that a substantial relationship exists between the contingency measures and environmental health. It is concluded that the COVID-19-induced lockdown has a positive impact on the global warming, a major issue of the 21st century.

Anaphylactic choc after sulphur Hexafluoride (SonoVue) injection and hypersensitivity to COMIRNATY vaccine, regarding to a case

Background: Use of ultrasound contrast agent spread increasingly in worldwide. Although few side effects are published, number of increase anaphylactic reaction is reported. A recent cautionary remark was published about polyethylene glycol (PEG) as culprit for some ultrasound contrast agent hypersensitivity (Krantz et al. JACIP. Avril 2020). A 22 years-old woman was referred for a severe anaphylaxis after a second injection of Sulphur Hexafluoride (SonoVue, Bracco, Milan, Italy). The investigation was performed to explore hepatic multinods. She already had presented with uncomfortable feeling, generalized pruritus and nausea few minutes after the first injection of Sonovue, 10 months earlier. Three months before, she has recieved also a first injection of SPIKEVAX without any reaction. As Sonovue and SPIKEVAX contain PEG, we looked for a hypersensitivity to PEG and RNAm COVID-19 vaccine. Method(s): Skin tests and basophil activation test (BAT) with expression of CD63 were performed for SonoVue, PEG 3350 and 4000 and COMIRNATY. Result(s): Skin tests were negative for all products except the undiluted IDT to COMIRNATY. BAT to PEG and SonoVue were negative but positive to COMIRNATY. Despite a fractioned administration of COMIRNATY, the patient presented with an acute urticaria few minutes after the last injection Conclusion(s): The relationship between PEG, RNAm vaccine, and hypersensitivity to SonoVue is once again highlighted by this new report. (Soni et al. J Am Soc Echocardiogr, Oct 2021).

Changes of atmospheric metal(loid) deposition from 2017 to 2021 at Mount Emei under China's air pollution control strategy

Acid deposition and particulate matter (PM) pollution have declined considerably in China. Although metal(loid) and acid deposition and PM have many common sources, the changes of metal(loid) deposition in China in the recent decade have not been well explored by using long-term monitoring. Therefore, we analyzed the dry and wet deposition of eleven metal(loid)s (including Al, As, Ba, Cd, Cu, Cr, Fe, Mn, Pb, Sr, and Zn) from 2017 to 2021 at Mount Emei, which is adjacent to the most economic-developed region in western China (Sichuan Basin (SCB)). Anthropogenic emissions contributed to over 80% of the annual wet deposition fluxes of metal(loid)s and acids (SO4 2-, NO3 -, and NH4 +) at Mount Emei, and the major source regions were the SCB, the Yunnan-Guizhou Plateau, and Gansu Province. Metal(loid) and acid deposition had similar seasonal variations with higher wet deposition fluxes in summer but higher wet deposition concentrations and dry fluxes in winter. The seasonal variations were partially associated with higher precipitation but lower pH in summer (968 mm and 5.52, respectively) than in winter (47 mm and 4.73, respectively). From 2017 to 2021, metal(loid) deposition did not decline as substantially as acid deposition (5.6%-30.4%). Both the annual total deposition fluxes and concentrations of Cr, Cu, Sr, Ba, and Pb were even higher in 2020-2021 than in 2017-2018. The inter-annual and seasonal changes implied the responses of metal(loid) deposition to anthropogenic emission changes were buffered (e.g., transformation, dilution, and degradation) by precipitation rates, acidity, natural emissions, and chemical reactions in the atmosphere, among others.Copyright © 2023 Elsevier Ltd

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10, PM2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime PM10, PM2.5, CO, and NO2 concentration during the lockdown in Colombo (Kandy) is –42.3% (–39.5%), –46% (–54.2%), –14.7% (–8.8%) and –82.2% (–80.9%), respectively. In both cities, the response of NO2 to the lockdown was the most sensitive. In contrast, daytime O3 concentration in Colombo (Kandy) has increased by 6.7% (27.2%), suggesting that the increase in O3 concentration was mainly due to a reduction in NOx emissions leading to lower O3 titration by NO. In addition, daytime SO2 concentration in Colombo has increased by 22.9%, while daytime SO2 concentration in Kandy has decreased by –40%. During the lockdown period, human activities were significantly reduced, causing significant reductions in industrial operations and transportation activities, further reducing emissions and improving air quality in two cities. The results of this study offer potential for local authorities to better understand the emission sources, assess the effectiveness of current air pollution control strategies, and form a basis for formulating better environmental policies to improve air quality and human health. © The Author(s).

Integral Assessment of Atmospheric Air Quality in the Largest Cities of Russia Based on TROPOMI (Sentinel-5P) Data for 2019–2020

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.

Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

TIME-SERIES ANALYSIS OF AIR QUALITY IN MAJOR CITIES IN THE PHILIPPINES DURING THE COVID-19 PANDEMIC THROUGH SENTINEL-5

Air pollution causes respiratory ailments and drives climate change. Air quality is driven by emissions from various sources, weather patterns, and transport of pollutants. Satellite analysis of pollutants in the atmosphere can provide temporally consistent and spatially wide measurements. In this study, the monthly concentrations of Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3) from the Sentinel-5 Tropospheric Monitoring Instrument (TROPOMI) were analyzed in four major cities in the Philippines, representing different climate types. Satellite-based measurements of land surface temperature and rainfall were used to investigate meteorological effects to air pollutants. Seasonal patterns were observed in the time series of NO2, O3 and CO alongside rainfall and LST. During the dry season, high LST and low precipitation is observed to be associated with increase in NO2, O3, and CO concentrations. On the other hand, wet seasons show decreases in concentrations of air pollutants, consistent with the washout effect. The NO2 average concentration in NCR is 1.9, 2.1, 2.3 times higher than in Metro Cebu, Davao City, and Legazpi City, respectively. In contrast, SO2 average concentration is highest in Legazpi City due to the nearby active volcano by a maximum factor of 1.8 compared to other cities. In addition, air quality changes brought about by community quarantines were examined since the onset of the COVID-19 crisis. Transition from the pre-quarantine period to the first lockdown shows sudden decrease by 28% in satellite-based retrievals of NO2 in NCR, mainly due to reduced anthropogenic emissions. As tiers of community quarantines were introduced, an increase in pollutant concentrations was observed, returning to pre-pandemic air quality as the guidelines ease physical and economic restrictions. Monitoring and analyzing the patterns in concentration of air pollutants in relation to meteorological and anthropogenic drivers can help in the air quality management in the country. © 43rd Asian Conference on Remote Sensing, ACRS 2022.

Copper Sulfate-Induced Diphenylamine for Rapid Colorimetric Point-of-Care Detection of Contagious Pathogens Combined with Loop-Mediated Isothermal Amplification

Here, we developed a copper sulfate (CuSO4)-initiated diphenylamine (DPA)-based colorimetric strategy coupled with loop-mediated isothermal amplification (LAMP) for rapid detection of two critical contagious pathogens, SARS-CoV-2 and Enterococcus faecium. To detect the DNA, acid hydrolysis of LAMP amplicons was executed, enabling the development of a blue color. In the LAMP amplicons, the bond between the purines and deoxyribose is extremely labile. It can be broken using 70% sulfuric acid followed by phosphate group elimination, which generates a highly active keto aldehyde group. CuSO4 plays an imperative role inducing DPA to rapidly react with the keto aldehyde group, producing an intense blue color within 5 min. Moreover, low quantities such as 103 copies μL-1 of SARS-CoV-2 RNA and 102 CFU mL-1 of E. faecium were successfully detected, revealing the advantages of the introduced method. To confirm practical applicability, multiplex detection of pathogens was performed using a foldable microdevice comprising reaction and detection zones. Various reactions such as DNA extraction, LAMP, and acid hydrolysis occurred in the reaction zone. Then, colorimetric reagents (DPA, CuSO4, and ethylene glycol) contained in the detection zone were mixed with the keto aldehyde group by simply folding the microdevice, which was heated at 65 °C for 5 min for colorimetric detection. An intense blue color was developed where the target DNA was present. These results indicate that the method proposed in this study is highly suitable for point-of-care applications, especially in resource-limited settings for the rapid detection of harmful pathogens. © 2023 American Chemical Society.

Sustainable policies for air pollution reduction after COVID‐19 pandemic: Lessons learnt from the impact of the different lockdown periods on air quality

Due to the COVID‐19 pandemic, governments imposed several mobility restrictions which can be used to evaluate their impact on air quality and generate better‐targeted policies to improve it. Therefore, this study aimed to define sustainable mitigation measures to reduce air pollution based on quantifying the impacts of the restrictions imposed during the COVID‐19 pandemic on air quality in Portugal. Thus, hourly concentrations of PM10, PM2.5, NO2, O3, CO and SO2 were obtained from the Portuguese Air Quality Monitoring Network. Data was then divided into six periods (2020–2021) and compared with the corresponding historical periods (2015–2019). Furthermore, the satellite data of NO2, CO, and absorbing aerosol index (AAI) from the sentinel‐5P TROPOMI was collected to complement the analysis conducted for the monitoring data. Overall, air quality improved in all study periods and areas, except in industrial sites. The satellite data corroborated the results herein achieved and thus validated the real effect of the measures adopted in the country during the pandemic on air quality. Sustainable policies to improve air quality could include remote (or hybrid) work whenever possible as a long‐term measure and prohibition of travelling between municipalities when an extraordinary event of high air pollution is predicted or occurs. Other policies should be adopted for industrial areas. Given this, and as the restrictive mobility measures had a strong effect on reducing air pollution, the post‐COVID era represents an opportunity for society to rethink future mobility and other emerging policies, that should favour softer and cleaner means of transportation.

Statistical assessment of COVID-19 lockdowns on ambient air quality, Himachal Pradesh and learnings for implementing clean technologies: insight from industrial town, India

PurposeThe current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.Design/methodology/approachThe air quality parameters under study are particulate matter10 (PM10), PM2.5, SO2 and NO2. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.FindingsThe findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO2, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.Research limitations/implicationsFuture research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.Originality/valueVery few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.Graphical abstract