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1.
Interdisciplinary Environmental Review ; 22(3-4):274-291, 2022.
Article in English | ProQuest Central | ID: covidwho-2197246

ABSTRACT

In populated and developing countries, governments consider the regulation and protection of the environment as a major task and should take into consideration the concept of smart environment monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, datasets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. So, forecasting air quality index using an intelligent environment system, which includes a machine learning model in order to predict air quality index for National Capital Region (NCR) was proposed. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3 were used. In recent years, machine learning in most emerging technology is used for prediction with 99.99% of accuracy by using historical data.

2.
Environmental Health ; 21:1-13, 2022.
Article in English | ProQuest Central | ID: covidwho-2196301

ABSTRACT

Background Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones. Objectives We examined influences of medium-term residential exposure to fine particulate matter (PM2.5), NO2, SO2, air temperature and precipitation on influenza incidence. Methods To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. Results In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM2.5 and NO2. Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m3 to 15.98 μg/m3. Conclusions Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.

3.
5th International Conference on Information and Communications Technology, ICOIACT 2022 ; : 210-214, 2022.
Article in English | Scopus | ID: covidwho-2191901

ABSTRACT

COVID-19 has plagued the world, one of which is Indonesia. During the COVID-19 pandemic, all anthropogenic activities are limited, including activities that cause air pollution, such as transportation and industrial activities. Nitrogen Dioxide (NO2) is one of the parameters of air pollution which has the main source of human activity. Therefore, this study aims to analyze the effect of the COVID-19 pandemic on changes in NO2 gas concentrations in the Yogyakarta Special Province. This study uses Sentinel 5-P satellite imagery data obtained through cloud computing on Google Earth Engine (GEE) to obtain NO2 gas concentration values. The results showed that there was a 3.7% decrease in the concentration of NO2 gas before and after the COVID-19 pandemic. The correlation result between the number of COVID-19 cases and the concentration of NO2 gas is 0.39, which means it has a weak correlation. © 2022 IEEE.

4.
2022 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2191695

ABSTRACT

The purpose of the study was to evaluate the variation of air quality parameters: PM10, PM2.5, NO2, and O3 in four districts of Lima-Peru (Carabayllo, San Juan de Lurigancho, Villa María del Triunfo, and Jesús María) in the period 2015-2019 and 2020-2021. Likewise, the ozone variability in the Carabayllo district was modelled. Pollutant concentration data were collected from the National Service of Meteorology and Hydrology of Peru (SENAMHI) from the 4 stations located in the aforementioned districts. The data was processed with the IBM SPSS Statistics v.25 software. A statistically significant decrease was observed between the 2015-2019 and 2020-2021 periods in pollutants PM10, PM2.5, and NO2, in the four monitoring stations, mainly because the country entered in a state of emergency (quarantine due to COVID -19). However, an increase in O3 was observed, attributed to the decrease in NOX concentrations. Finally, the gamma generalized linear model represented 87.6% of the ozone variability in the Carabayllo district, showing a good fit for the field data. © 2022 IEEE.

5.
Geophysical Research Letters ; 49(23), 2022.
Article in English | ProQuest Central | ID: covidwho-2185563

ABSTRACT

A unified framework that connects emissions with satellite‐observed column amounts is derived from first principles. The emission information originates from the inner product of the horizontal wind and the gradient of column amount, which is more accurate than the horizontal flux divergence as used in previous studies. Additionally, the topographical and chemical effects are accounted for through fitted scale height and chemical lifetime. This framework is applied to derive NOx and CO emissions over the CONUS from TROPOspheric Monitoring Instrument NO2 and CO observations. High‐resolution (0.04°) emission mapping over the CONUS reveals unprecedented details, including CO emissions in major cities and NOx emissions from large cities, power plants, and major roadways. Monthly resolved NOx emissions show decrease and rebound after the COVID‐19 pandemic. This framework is integrated with the physical oversampling algorithm and can be readily applied to other products from the new‐generation satellite instruments.Alternate :Plain Language SummarySatellites usually measure the vertically integrated column amount of atmospheric species from space. For short‐lived species like nitrogen oxides, the observed column amount indicates location and strength of emission sources. However, atmospheric dispersion smears the relationship between emission and column amount as the lifetime of species gets longer. This study directly maps emission based on the principle of mass balance. Namely, the spatial gradient of column amount should align with horizontal wind if there is an emission. Additionally, topography and chemical reaction may cause spatial gradients of column amount that are unrelated to emissions and are accounted for. Unprecedented details in the emission of air pollutants are unveiled by applying this approach to the TROPOspheric Monitoring Instrument products.

6.
Air Qual Atmos Health ; : 1-19, 2022.
Article in English | Web of Science | ID: covidwho-2174978

ABSTRACT

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NO(x)) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

7.
Acta Geophysica ; 2022.
Article in English | Scopus | ID: covidwho-2174887

ABSTRACT

The lockdown in 2020 implemented due to the SARS-CoV-2 pandemic has resulted in a significant improvement in air quality at a global scale. Nationwide lockdown also considerably improved air quality at a local scale, especially in cities which were almost completely shut down during the first coronavirus wave, with nearly no activity. We tested the hypothesis that a reduction in the intensity of vehicle traffic causes a drastic decrease in urban air pollution at a local scale. We focused on two urban agglomerations, Warsaw and Cracow, in Poland. Data of the concentrations of traffic-related sources, namely NOx, PM10, and PM2.5, obtained from two air pollution monitoring stations were analyzed for the years 2020 and 2021, during which lockdown and pandemic restrictions were in effect, and for 2019, as a reference. In the years 2020–2021, the average annual concentration of NOx was decreased by ~ 19%, PM2.5 by ~ 19%, and PM10 by ~ 18% in Warsaw, while in Cracow the average annual concentration of NOx was decreased by ~ 16%, PM2.5 by ~ 22%, and PM10 by ~ 2%, compared to 2019. The contribution from traffic-related sources to the overall level of air pollution was estimated. The results indicated that ~ 30 µg/m3 of PM10, ~ 15 µg/m3 of PM2.5, and ~ 120 µg/m3 of NOx in Cracow, and ~ 20 µg/m3 of PM2.5 in Warsaw originate from moving vehicles. The nationwide lockdown allowed us to conduct this study to understand how a reduction in local traffic emissions can decrease ambient air pollution levels. © 2022, The Author(s).

8.
Clean Technol Environ Policy ; : 1-14, 2022.
Article in English | Web of Science | ID: covidwho-2174398

ABSTRACT

Atmospheric nitrogen oxides ( NOx = NO + NO2 ) are key pollutants and short-lived climate forcers contributing to acid rain, photochemical smog, aerosol formation and climate change. Exposure to nitrogen dioxide ( NO2 ) emitted mainly from transportation, causes adverse health effects associated with respiratory illnesses and increased mortality even at low concentration. Application of titanium dioxide ( TiO2 )-based photocatalysis in urban environment is a new air cleaning solution, activated by sunlight and water vapour to produce OH radicals, able to remove NOx and other pollutants from the planetary boundary layer. This study is a large-scale evaluation of NOx removal efficiency at a near-road environment with applied photocatalytic NOxOFF technology on an urban road west of Copenhagen, thus supporting local municipality in meeting their clean-air Agenda 2030. The photocatalytic NOxOFF granulate containing TiO2 nanoparticles was applied on an asphalt road in July 2020 and ambient NOx was measured during a six-month monitoring campaign. It is the first NOx monitoring campaign carried out at this road and specific efforts have been devoted to evaluate the reduction in ambient NOx levels with NOxOFF-treated asphalt. Several methods were used to evaluate the photocatalytic effect, taking into account analysis limitations such as the short reference period prior to application and the highly uncertain measurement period during which SARS-CoV-2 lockdown measures impacted air quality. There was no statistically significant difference in NOx concentrations between the reference period and the photocatalytic active period and NO removal efficiency resulted in - 0.17 (+/- 1.27). An upper limit removal of 17.5% NOx was estimated using a kinetic tunnel model. While NO2 comparison with COPERT V street traffic model projection was roughly estimated to decrease by 39% (+/- 38%), although this estimate is subject to high uncertainty. The observed annual mean NO2 concentration complies with Frederiksberg clean-air Agenda 2030 and air quality standards. GRAPHICAL : A graphical illustrating the air cleaning properties of TiO2 -based photocatalytic-treated asphalt.

9.
BR Wells Rice Research Studies Arkansas Agricultural Experiment Station, University of Arkansas System ; 685:264-268, 2022.
Article in English | CAB Abstracts | ID: covidwho-2170127

ABSTRACT

Seeking to fine-tune nitrogen (N) application, increase economic returns, and decrease environmental N loss, some Arkansas rice (Oryza sativa L.) producers are turning away from blanket N recommendations based on soil texture and cultivar and using the Nitrogen Soil Test for Rice (N-STaR) to determine their field-specific N rates. In 2010, Roberts et al. correlated years of direct steam distillation (DSD) results obtained from 0- to 18-in. soil samples to plot-scale N response trials across the state to develop a field-specific, soil-based N test for Arkansas rice. After extensive small-plot and field-scale validation, N-STaR is available to Arkansas farmers for both silt loam and clay soils. Samples submitted to the N-STaR Soil Testing Lab in 2021 were summarized by county and soil texture, totaled 21 fields across 9 Arkansas counties, and were from 6 clay and 15 silt loam fields. Depressed sample submissions were again observed likely due to another wet spring and lingering effects of the COVID-19 pandemic. The N-STaR N-rate recommendations for samples were compared to the producer's estimated N rate, the 2021 Recommended Nitrogen Rates and Distribution for Rice Cultivars in Arkansas, and the standard Arkansas N-rate recommendation of 150 lb N/ac for silt loam soils and 180 lb N/ac for clay soils. Each comparison was divided into 3 categories based on a decrease in recommendation, no change in recommended N rate, or an increase in the N rate recommendation. In all 3 comparisons, county, but not soil texture, was a significant factor (P < 0.04) in observed decreases in N recommendation strategies demonstrating variations in the soil's ability to supply N across the state. Further stressing the potential N cost savings opportunities, reductions greater than 30 lb N/ac were recommended by N-STaR in 71%, 50%, and 74% of fields in the standard, estimated, and cultivar comparisons, respectively.

10.
Proceedings of the International Academy of Ecology and Environmental Sciences ; 12(4):269-280, 2022.
Article in English | ProQuest Central | ID: covidwho-2169635

ABSTRACT

The lockdown was implemented by the government of India during the pandemic period due to Covid-19. This paper presents the effect of lockdown on the air quality index and various pollutants in five major locations in the Chennai city of Tamil Nadu. The air pollutants such as PM10, PM2.5, SO2 and NO2 from the monitoring stations were analyzed from 2018 to 2019 (pre-Covid period) and from 2020 to 2021 (during-Covid period). The results demonstrated that the concentration of PM10 and PM2.5 reduced about 48% and 39% respectively. Similarly, significant reduction in the pollutants SO2 (-25%) and NO2 (-10%) has been observed. In the same way, AQI level before and during lockdown in Chennai city was observed satisfactory to moderate categories. The maximum reduction in AQI was observed in Adyar (-50.38%), followed by Nungambakkam (-44.18%), TNagar (-40.31%), Anna Nagar (-39.98%) and Kilpauk (-30.74%). Overall study implies that regulatory measures in a certain location in a suitable time period control the pollution and protects the environment.

11.
Journal of Health Research and Reviews in Developing Countries ; 9(1):22-29, 2022.
Article in English | ProQuest Central | ID: covidwho-2201979

ABSTRACT

Aim: This study aimed to describe the clinical characteristics, survival outcome, and its correlation with biochemical parameters in coronavirus disease-2019 (COVID-19)-infected patients with end-stage kidney disease (ESKD). Materials and Methods: A prospective observational study was on hospitalized patients with confirmed COVID-19 infection from September 1, 2020 to October 31, 2020. Data related to demographics, baseline history of comorbid conditions, dialysis-specific data, details on hospital admissions, COVID-19 treatment regimen, laboratory investigations, computed tomography (CT) severity score, COVID-19 Reporting and Data System score, and clinical outcomes (improved/death), duration of hospital stay, oxygen/vasopressor support were collected. Results: A total of 216 ESKD patients with COVID-19 infection were included in this study. The median age was 48.0 years (74.5% men, 25.5% women). Severe acute respiratory infection (44.7%), hypertension (28.2%), and type 2 diabetes mellitus (22.4%) were the most common comorbidities. Elevated levels of serum creatinine (9.3 mg/dL) and blood urea nitrogen (84.8 mg/dL) were observed in the patients with COVID-19 infection. The change in mean levels of serum creatinine and estimated glomerular filtration rate from baseline to post-treatment was significant (0.9 [95% CI: 0.7, 1.1;P < 0.001] and 3.4 [95% CI: 3.2, 3.6;P < 0.001], respectively). Approximately, 79.6% (n = 172) of patients improved post-treatment. Serum creatinine (1.786, 95% CI: 1.031, 3.095;0.039) and ferritin levels (51.959, 95% CI: 7.901, 341.685;P < 0.001) remained significantly and independently associated with survival. The median time to clinical survival was 17.0 days. Conclusion: Serum creatinine and ferritin levels were independently associated with survival.

12.
PeerJ ; 2023.
Article in English | ProQuest Central | ID: covidwho-2203235

ABSTRACT

Background Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India. Methodology In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19. Results The current findings for gaseous pollutants reveal that concentration declined in the range of 51%–83% (NO), 40%–69% (NOx), 31%–60% (NO2), and 25%–40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0–1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods. Conclusion The air quality values for pre-lockdown were calculated to be "poor category to "very poor” category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as "satisfactory,” except for Northeast Delhi, which displayed the "moderate” category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes.

13.
Earth System Science Data ; 15(1):189-209, 2023.
Article in English | ProQuest Central | ID: covidwho-2202607

ABSTRACT

Having a prediction model for air quality at a low computational cost can be useful for research, forecasting, regulatory, and monitoring applications. This is of particular importance for Latin America, where rapid urbanization has imposed increasing stress on the air quality of almost all cities. In recent years, machine learning techniques have been increasingly accepted as a useful tool for air quality forecasting. Out of these, random forest has proven to be an approach that is both well-performing and computationally efficient while still providing key components reflecting the nonlinear relationships among emissions, chemical reactions, and meteorological effects. In this work, we employed the random forest methodology to build and test a forecasting model for the city of Buenos Aires. We used this model to study the deep decline in most pollutants during the lockdown imposed by the COVID-19 (COronaVIrus Disease 2019) pandemic by analyzing the effects of the change in emissions, while taking into account the changes in the meteorology, using two different approaches. First, we built random forest models trained with the data from before the beginning of the lockdown periods. We used the data to make predictions of the business-as-usual scenario during the lockdown periods and estimated the changes in concentrations by comparing the model results with the observations. This allowed us to assess the combined effects of the particular weather conditions and the reduction in emissions during the period when restrictions were in place. Second, we used random forest with meteorological normalization to compare the observational data from the lockdown periods with the data from the same dates in 2019, thus decoupling the effects of the meteorology from short-term emission changes. This allowed us to analyze the general effect that restrictions similar to those imposed during the pandemic could have on pollutant concentrations, and this information could be useful to design mitigation strategies.The results during testing showed that the model captured the observed hourly variations and the diurnal cycles of these pollutants with a normalized mean bias of less than 6 % and Pearson correlation coefficients of the diurnal variations between 0.64 and 0.91 for all the pollutants considered. Based on the random forest results, we estimated that the lockdown implied relative changes in concentration of up to -45% for CO, -75% for NO, -46% for NO2, -12% for SO2, and -33% for PM10 during the strictest mobility restrictions. O3 had a positive relative change in concentration (up to an 80 %) that is consistent with the response in a volatile-organic-compound-limited chemical regime to the decline in NOx emissions. The relative changes estimated using the meteorological normalization technique show mostly smaller changes than those obtained by the random forest predictive model. The relative changes were up to -26% for CO, up to -47% for NO, -36% for NO2, -20% for PM10, and up to 27 % for O3. SO2 is the only species that had a larger relative change when the meteorology was normalized (up to 20 %). This points out the need for accounting not only for differences in emissions but also in meteorological variables in order to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. We believe that the model itself can also be a valuable contribution to a forecasting system in the city and that the general methodology could also be easily applied to other Latin American cities as well. We also provide the first O3 and SO2 observational dataset in more that a decade for a residential area in Buenos Aires, and it is openly available at 10.17632/h9y4hb8sf8.1 .

14.
Environ Sci Technol ; 2022 Nov 23.
Article in English | MEDLINE | ID: covidwho-2185450

ABSTRACT

Disparities in exposure to traffic-related air pollution have been widely reported. However, little work has been done to simultaneously assess the impact of various vehicle types on populations of different socioeconomic/ethnic backgrounds. In this study, we employed an extreme gradient-boosting approach to spatially distribute light-duty vehicle (LDV) and heavy-duty truck emissions across the city of Toronto from 2006 to 2020. We examined associations between these emissions and different marginalization indices across this time span. Despite a large decrease in traffic emissions, disparities in exposure to traffic-related air pollution persisted over time. Populations with high residential instability, high ethnic concentration, and high material deprivation were found to reside in regions with significantly higher truck and LDV emissions. In fact, the gap in exposure to traffic emissions between the most residentially unstable populations and the least residentially unstable populations worsened over time, with trucks being the larger contributor to these disparities. Our data also indicate that the number of trucks and truck emissions increased substantially between 2019 and 2020 whilst LDVs decreased. Our results suggest that improvements in vehicle emission technologies are not sufficient to tackle disparities in exposure to traffic-related air pollution.

15.
Transportation Research Part D: Transport and Environment ; 115:103572, 2023.
Article in English | ScienceDirect | ID: covidwho-2165912

ABSTRACT

The transport sector has been identified as one of the main contributors to nitrogen dioxide (NO2) pollution in Ireland. This research develops an enhanced Wind Sector Land Use Regression (WS-LUR) model to estimate NO2 concentrations across Ireland, in areas where air pollution monitoring is not available. The model incorporates details of the vehicle fleet breakdown to weight vehicle type flows based on the emission rates of the vehicle type, differentiating routes with varying proportions of heavier emitting vehicles. In 2008, car taxation underwent a significant change from an engine size based system to a carbon dioxide (CO2) emission rate based system resulting in a significant transition towards diesel fuelled vehicles. A mitigation strategy to remove diesel fuelled vehicles from the public service vehicle fleet was tested achieving predicted NO2 reductions in the range of 0.3 μg/m3 to 1.9 μg/m3. The impact of COVID-19 on NO2 concentration levels was also investigated.

16.
Abu Dhabi International Petroleum Exhibition and Conference 2022, ADIPEC 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2162748

ABSTRACT

Reservoir surveillance and production optimization will remain at the forefront of company strategies in the new post-COVID19 environment. We anticipate that companies will focus more on producing assets and go the route of production enhancement rather than exploration. Accordingly, production logging will remain an important surveillance method in evaluating and strategizing production-optimization schemes pertaining to flow-characterization from reservoir-to-wellbore. This work is culmination of operational and technical excellence that enabled the revival of a loaded-up well through simultaneous lifting-and-logging technique. Conventionally, wireline is the preferred mode of conveyance for production-logging;however, well must be continuously flowing throughout acquisition timeframe. Kicking-off the well using nitrogen-lift and then bringing in wireline-unit for production-logging in Well A-4 was not feasible as previous attempts confirmed well to load-up in few hours post-offloading. Therefore, success of this project was heavily dependent on initial planning stage, which accounted for all available data including production-history, well-events, intervention-details, fluid analysis and well load-up behavior. Next, a multi-domain approach was adopted while bringing-out each domain from its silos and strategize collectively to simultaneously kickoff the well with nitrogen and acquire real-time downhole production-logging data through smart-coiled-tubing (CT). This was first implementation of concurrent lifting and logging operation in Pakistan. By deploying the approach mentioned above through smart CT (using optical-telemetry-link inside the CT-string coupled with downhole-assembly), synchronized lifting-and-logging operation was carried-out successfully. Well was observed to swiftly go back to load-up conditions post-kickoff;however, continuous well dynamics monitoring downhole enabled us to log perforated interval across multiple time domains. Well was activated through CT nitrogen-injection but depicted continuous loading tendency, which was captured downhole in form of flow-transients. Real-time job optimization ensured vigilant monitoring and selection of right-time to acquire meaningful zonal-contribution data for evaluation and diagnostic solutions. Finally, operational excellence was complemented through technical data analysis and interpretation, integrating passes data with transients and stationary measurements. Ultimately, acquired data analyzed using an integrated lens involving fluid velocities, downhole density, temperature, and water hold up data. Consequently, enabling us to decipher gas and water-entries on a zonal-basis across perforated sandstone reservoir. Copyright © 2022, Society of Petroleum Engineers.

17.
Pediatrics ; 150, 2022.
Article in English | ProQuest Central | ID: covidwho-2162653

ABSTRACT

PURPOSE OF THE STUDY: Respiratory viruses, air pollutants, and aeroallergens are all implicated as triggers for pediatric asthma symptoms. The current study sought to determine whether changes in respiratory viruses, air pollutants, or aeroallergens during the coronavirus disease 2019 (COVID-19) pandemic were associated with decreased asthma exacerbations. In a prior study, the authors found that during the first months following public health interventions to limit the spread of COVID-19, asthma visits and steroid prescriptions decreased by more than 80%, with a corresponding decrease in rhinovirus infections, without noted changes in air pollution. STUDY POPULATION: The authors reviewed asthma patient encounter data from January 1 to December 31 for the years 2015 through 2020 from the Children's Hospital of Philadelphia Care Network, including 48 outpatient primary care and specialty care sites, 4 urgent care sites, 15 community hospitals, and a 557-bed quaternary care center. Demographic data for outpatient, inpatient, and video visits were characterized by patient sex, race, ethnicity, birth year cohort, and payer type. 2020 data for 28 157 patients were compared with 2015 to 2019 data for 88 039 patients. METHODS: Health care utilization and respiratory viral testing data for the period between January 1, 2015 and December 31, 2020 were extracted from the Children's Hospital of Philadelphia Care Network electronic health record. Air pollution data, including particulate pollution, ozone, and nitrogen dioxide, were obtained from US Environmental Protection Agency databases. Tree, grass, weed, and mold aeroallergen data were obtained from a National Allergy Bureau-certified monitoring station. Summary statistics for rates of encounters and asthma-related prescriptions from 2020 were compared with those from 2015 to 2019. Comparisons were made between prelockdown, lockdown, and phased reopening periods for public health measures in Philadelphia and surrounding counties. RESULTS: During the COVID-19 pandemic, weekly positive tests for influenza A, influenza B, RSV, and rhinovirus were lower than 2015 to 2019 historical averages. Air pollution and aeroallergen trends did not substantially change during the COVID-19 pandemic compared with historic and seasonal average data. CONCLUSIONS: Viral respiratory infections are a primary driver of pediatric asthma exacerbations.

18.
Stochastic Environmental Research and Risk Assessment ; 2022.
Article in English | Scopus | ID: covidwho-2158038

ABSTRACT

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60−90%. The NO2 concentration decreased by approximately 50%, while O3 concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO2 concentration decreased exponentially, whereas O3 concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

19.
5th SAENIS TTTMS Thermal Management Systems Conference, TTTMS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2155559

ABSTRACT

The COVID-19 pandemic has driven the population to be extremely vigilant towards personal as well as shared hygiene necessitating use of facemask, maintaining social distancing, frequent hand wash and vehicle sanitization. Humans are exposed to pollutants such as Particulate Matter (PM), oxide of Sulphur (SOx), oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Total Volatile Organic Compound (TVOC) and pathogens such as fungi, bacteria, viruses (FBV) either through air or by direct contact with contaminated surfaces. In a vehicle cabin, occupants are exposed to both fresh and recirculating air through air-conditioning system and they also come in contact with touch points such as seats, steering wheel and armrest, which may be contaminated. In order to safeguard the occupants, Vehicle Interior Air Quality (VIAQ) enhancers like high efficiency cabin air filters (N95/ HEPA) with activated carbon/ anti-allergen/ anti-microbial layers, ionizers and anti-bacterial trims are being deployed by OEMs. In many cases, validation of these VIAQ enhancers is done on a bench setup. Once a VIAQ enhancer is integrated into the vehicle architecture, a series of additional subjective and objective validations will need to be carried out, which is the topic of this study. This paper proposes a novel two pronged approach to validate a VIAQ enhancer. The two approaches are subjective and objective assessments on the vehicle. The subjective assessment comprises calibration of human noses as per EN 13275 standard, training the calibrated noses for identification of odor character (OC), quantifying odor intensity (OI) and its hedonic tone (HT) as per VDI 3882. Whereas, the objective assessment comprises of use of handheld equipment for sampling and measurement of pollutants such as PM, SOx, NOx, CO, O3 and TVOC. With the above novel approach, the effectiveness of VIAQ enhancers can be assessed prior to its deployment on vehicle programs for real world application. Adopting this approach will ensure the vehicle cabin is maintained within permissible limits for measurable parameters (PM, SOx, NOx, CO, O3, TVOC, FBV) and subjectively perceived odor (OC, OI and HT). © 2022 SAE International. All rights reserved.

20.
Clinical Nutrition ESPEN ; 2022.
Article in English | ScienceDirect | ID: covidwho-2149517

ABSTRACT

Background Malnutrition, as defined by the World Health Organization (WHO), includes undernutrition. In the Philippines, malnutrition is common due to several factors. The nutritional biomarkers can be used as an alternative indicator of dietary intake and nutritional status that can detect deficiencies in support to clinical management of COVID-19 patients. Apart from that, biomarkers are potentially useful for screening, clinical management, and prevention of serious complications of COVID-19 patients. Serum albumin, c-reactive protein (CRP), leukocyte count, lymphocyte count, blood urea nitrogen to compute/the nutritional prognostic indices (Prognostic nutritional index (PNI) score, Blood Urea Nitrogen/Albumin ratio (BAR) and C-reactive protein/Albumin ratio (CAR). Objectives To compare the nutritional biomarkers of patients with COVID-19 based on case severity and determine the nutrition prognostic indices and associate to patients’ clinical outcome during hospital stay. Methods A single center, cross-sectional study was performed between June 2021 to August 2021 in a COVID-19 designated referral center in CALABARZON which comprised of 167 patients as part of the study. Clinicodemographic profile including patients’ age, sex, co-morbidities, weight, height, laboratory, and serum biomarkers during the first 48 hours of admission (serum albumin, leukocyte count, lymphocytes count, CRP, and BUN) were collated wherein the nutritional prognostic indices were computed and analyzed. Clinical outcomes of the patients were based on the patients’ final diagnoses (recovered, length of hospital stay, progression of severity and mortality). Results 167 non-critically ill COVID-19 patients were included in the analysis, of which 52.7% are admitted under the COVID-19 severe group and 47.3% for COVID-19 Mild/Moderate. Mostly are male (53.3%) with an average BMI of 24.26 (SD=3.52) and have hypertension (55.1%) and diabetes (42.5%). Among the nutritional biomarker, albumin (p=0.028;p=0.004), TLC (p=0.013;p=0.005) and BUN (p=0.001;p=<0.001) were shown to be significantly associated with progression of severity and mortality. Univariate logistic regression analysis showed the following nutritional prognostic score were correlated: (1) progression of COVID-19 severity: PNI score (OR 0.928, 95% CI 0.886, 0.971, p=<0.001), and BAR value (OR 1.130, 95% CI 1.027, 1.242, p=0.012);(2) Mortality: PNI score (OR 0.926, 95% CI 0.878, 0.977, p=0.005), CAR (OR 1.809, 95% CI 1.243, 2.632, p=0.002), and BAR (OR 1.180, 95% CI 1.077, 1.292, p=<0.001). The average length of stay of COVID-19 patients was 12 days (SD=7.72). However, it does not show any significant correlation between any nutritional biomarker, prognostic indices and length of hospital stay. Conclusion This study demonstrated that deranged level of nutritional biomarkers can affect patient’s COVID-19 severity and associated with patient’s clinical outcome. Low albumin (<2.5g/dL), low level of TLC (<1500 cells/mm3), elevated BUN (>7.1 mmol/L) are associated with patient’s case severity progression and mortality while low PNI score (<42.49), high BAR value (>2.8) and CAR value (>2.04) provided an important nutritional prognostic information and could predict mortality which can be a useful parameter in admission, hence it is recommended to screen all COVID-19 patients to reduce mortality.

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