Showing papers by "Prashant Kumar published in 2022"

MCDA techniques used in optimization of weights and ratings of DRASTIC model for groundwater vulnerability assessment

Abstract: DRASTIC is a very simple and common model used for the assessment of groundwater to contamination. This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment. The Ohio Water Well Association (OWWA) developed DRASTIC model in 1987. Over the years, several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination. This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters. The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique, which is the best technique for the consensus-building of experts, but it lacks scientific explanations. Over the years, several optimization techniques have been used to optimize these weights and ratings. This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings. The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed. The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data, the pilot study area and the level of required accuracy for earmarking the vulnerable regions. It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time, efforts, resources, and implementation costs.

Charting the intellectual structure of customer experience research

Abstract: PurposeContemporary firms are increasingly focusing on enhancing the customer experience (CX) to gain a sustainable competitive advantage. However, despite the rapid growth of CX research from both the customer's and the firm's perspectives, the intellectual structure of CX research remains tenuous, thus requiring further investigation. Addressing this gap, the authors review and map the existing corpus of CX literature, from which important implications are drawn.Design/methodology/approachAfter inventorizing the CX literature (1997–2021), the authors apply bibliometric (e.g. co-citation) analysis to identify key CX articles and pertinent CX-publishing journals, followed by the identification of key CX research themes through network analysis.FindingsThe authors first document chief CX-publishing journals and articles and identify their respective contributions. The authors, then, derive five core CX themes, including CX through the customer journey, S-D Logic-informed CX, interactive service–based CX, CX in the servicescape and CX and consumption. The authors conclude by developing an agenda for future CX research based on the study findings.Originality/valueBy offering a pioneering analysis of the corpus of CX research (1997–2021), these analyses offer a pertinent theoretical contribution.

Implementation of green infrastructure for improving the building environment of elderly care centres

Abstract: The elderly population is relatively vulnerable to air pollution and thermal stress due to their low mobility and high prevalence of chronic disorders. Appropriate green infrastructure (GI) deployment can improve both the indoor and outdoor air quality and thermal environments of elderly care centres (ECCs), yet a systematic review on this topic area is lacking. This review aims to fill this gap by investigating the impacts of GI on ECC building environment and presents the approaches for integrating GI into the building environment design. We discussed the significance of linking air quality with the thermal environment to ECCs and the effects of GI on the elderly's physical health. We investigated the key design considerations for GI in ECC buildings (e.g., spatial layout, species, aesthetics and fire prevention). Also, the diversity of monitoring and modelling approaches for evaluating the benefits of GI in indoor and outdoor environments was assessed. Finally, we evaluated the associated challenges and provided design recommendations for improving the environments in and around the ECC buildings (e.g., bedrooms, indoor gardens, green roofs and courtyards). The quantitative evidence for linking GI with indoor and outdoor air pollution and extreme heat around the ECC buildings are limited. However, this evidence-base is important for providing generic advice to the building designers and the elderly. Further studies such as the evaluation criteria and monitoring standard are required to develop holistic design recommendations for ECC buildings. The empirical research about the social and economic impacts is also necessary to facilitate the sustainable development of the ageing societies.

Quantifying co-benefits and disbenefits of nature-based solutions targeting Disaster Risk Reduction

Abstract: Nature-based Solutions function (NBS) as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so-called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing co-benefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selection and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It builds on methods and frameworks from existing NBS literature and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and disbenefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects.

Impact of ionizers on prevention of airborne infection in classroom

Abstract: Abstract Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used.

Impact of all‐sky water vapour channel radiance from INSAT‐3D/3DR satellite over South Asia region using WRF model

Abstract: The objective of this study is to explore and present a methodology to assimilate all‐sky water vapour (WV) radiance from Indian geostationary satellites (INSAT‐3D and INSAT‐3DR) in the Weather Research and Forecasting (WRF) model. For all‐sky assimilation, hydrometeors are considered as control variables by adding in background error covariance. Additionally, this study uses the application of Global Satellite based Inter‐Calibration System (GSICS) based bias correction mechanism on WV radiances before their assimilation. To fulfil these objectives, three sets of experiments have been performed with and without WV radiance assimilation for the month of July 2018 over the South Asia region. The impact assessment of assimilation has been performed by comparing radiative transfer (RT) model‐simulated analysed and predicted brightness temperature against independent satellite observations from SAPHIR (Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie) and MHS (Microwave Humidity Sounder) sensors. Results not only show the number of assimilated observations increased significantly (∼250%) in all‐sky assimilation compared to clear‐sky assimilation but also present that the all‐sky analyses are closer to actual satellite observations. Due to the multivariate nature of variational data assimilation, noteworthy changes are also noticed in hydrometeors analyses in all‐sky assimilation. The short‐range forecasts confirm the positive impact of all‐sky assimilation as compared to clear‐sky assimilation when verified against SAPHIR and MHS measurements.

Did unprecedented air pollution levels cause spike in Delhi’s COVID cases during second wave?

Abstract: The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Pan-cancer quantitation of epithelial-mesenchymal transition dynamics using parallel reaction monitoring-based targeted proteomics approach

Abstract: Epithelial-mesenchymal transition (EMT) is a dynamic and complex cellular process that is known to be hijacked by cancer cells to facilitate invasion, metastasis and therapeutic resistance. Several quantitative measures to assess the interplay between EMT and cancer progression are available, based on large scale genome and transcriptome data. However, these large scale multi-omics studies have repeatedly illustrated a lack of correlation in mRNA and protein abundances that may be influenced by diverse post-translational regulation. Hence, it is imperative to understand how changes in the EMT proteome are associated with the process of oncogenic transformation. To this effect, we developed a parallel reaction monitoring-based targeted proteomics method for quantifying abundances of EMT-associated proteins across cancer cell lines. Our study revealed that quantitative measurement of EMT proteome which enabled a more accurate assessment than transcriptomics data and revealed specific discrepancies against a backdrop of generally strong concordance between proteomic and transcriptomic data. We further demonstrated that changes in our EMT proteome panel might play a role in tumor transformation across cancer types. In future, this EMT panel assay has the potential to be used for clinical samples to guide treatment choices and to congregate functional information for the development and advancing novel therapeutics.

Noise and air pollution during Covid-19 lockdown easing around a school site

Abstract: During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM1, PM2.5, and PM10 (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO2) and ground-level ozone (O3) (–0.68 < r < –0.78) as NO2 is a precursor of O3. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases.

Exposure assessment of PM2.5 in temple premises and crematoriums in Kanpur, India

Abstract: Regular use of incense and earthen lamps in temples leads to the release of particulate matter (PM), airborne flecks, and gaseous pollutants. Similarly, the cremation of dead bodies using timber and other accessories such as incense, organic chemicals containing carbon, and clothes generates air pollutants. It is currently unclear how much emissions and exposure these activities may lead. This work attempts to fill this gap in our understanding by assessing the associated emissions of PM2.5 and the corresponding exposure. Ten temples and two cremation grounds were considered for the sampling of PM2.5. The average PM2.5 concentration at the ten temples and the two crematoriums was found to be 658.30 ± 112.63 µg/m3 and 1043.50 ± 191.63 µg/m3, respectively. The range of real-time PM2.5 data obtained from the nearest twelve stations located in the vicinity was 113-191 µg/m3. The exposure assessment in terms of deposition dose was carried out using the ICRP model. The maximum and minimum total respiratory deposition dose rate for PM2.5 for temples was 175.75 µg/min and 101.15 µg/min, respectively. For crematoriums, the maximum and minimum value of same was 252.3 µg/min and 194.31 µg/min, respectively, for an exposure period of 10 min.

Particulate Matter Forecasting Using Different Deep Neural Network Topologies and Wavelets for Feature Augmentation

Abstract: The concern about air pollution in urban areas has substantially increased worldwide. One of its main components, particulate matter (PM) with aerodynamic diameter of ≤2.5 µm (PM2.5), can be inhaled and deposited in deeper regions of the respiratory system, causing adverse effects on human health, which are even more harmful to children. In this sense, the use of deterministic and stochastic models has become a key tool for predicting atmospheric behavior and, thus, providing information for decision makers to adopt preventive actions to mitigate air pollution impacts. However, stochastic models present their own strengths and weaknesses. To overcome some of disadvantages of deterministic models, there has been an increasing interest in the use of deep learning, due to its simpler implementation and its success on multiple tasks, including time series and air quality forecasting. Thus, the objective of the present study is to develop and evaluate the use of four different topologies of deep artificial neural networks (DNNs), analyzing the impact of feature augmentation in the prediction of PM2.5 concentrations by using five levels of discrete wavelet transform (DWT). The following types of deep neural networks were trained and tested on data collected from two living lab stations next to high-traffic roads in Guildford, UK: multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolutional neural network (1D-CNN) and a hybrid neural network composed of LSTM and 1D-CNN. The performance of each model in making predictions up to twenty-four hours ahead was quantitatively assessed through statistical metrics. The results show that wavelets improved the forecasting results and that discrete wavelet transform is a relevant tool to enhance the performance of DNN topologies, with special emphasis on the hybrid topology that achieved the best results among the applied models.

Tunning of rheological and magnetic properties of Ni doped magnetite based magnetic nanofluid

Abstract: The effect of nickel doping on the structural and magneto-rheological properties of Fe3O4 based nanomagnetic fluids (NMFs) having the composition NixFe3-xO4 (0 ≤ x ≤ 0.8) has been studied. The structural and compositional analysis confirmed the nanoscale regime (∼10 nm) of samples prepared by chemical co-precipitation method and structural analysis was performed by XRD pattern to ascertain the effect of Ni doping. From the UV–Visible spectroscopy studies, the energy band gap was found to be in the range 2.2–3.0 eV with increase in Ni concentration. The magnetic measurements reveal the lower values of coercivity and remanence. The magneto-rheological measurements confirm that in all samples shows a non-Newtonian shear thinning behavior and the increase of viscosity and dipolar interactions while increasing the magnetic field. Whereas in absence of field, higher grain boundaries enhance the interlayer friction and higher viscosity with Ni doping.

Clinical presentation of patients with seasonal influenza and pandemic influenza A (H1N1-2009) requiring hospitalisation.

Abstract: BACKGROUND A sudden increase in the number of novel influenza A virus (pH1N1-2009) infection prompted us to compare the clinical presentation and outcomes of patients infected with pH1N1-2009 and seasonal influenza A virus during the post-pandemic phase. METHODS During the period August 13 to September 27, 2010, case records of 106 patients with severe influenza like illness (ILI) and respiratory complications who underwent diagnostic testing by real-time polymerase chain reaction (RT-PCR) for confirmation of pH1N1-2009 were retrospectively studied. RESULTS Nineteen (17.9%) patients were tested positive for pH1N1-2009 and 78 (73.6%) were tested positive for seasonal influenza A virus. The mean age of patients infected with pH1N1-2009 was 45.2 +/- 15.3 years (range of 22 to 80 years). Common presenting symptoms included fever in 17 (89.4%), cough in 16 (84.2%), myalgia in 15 (78.9%) and breathlessness in 10 (52.6%) patients. The most common comorbidities included bronchial asthma/bronchitis/chronic obstructive pulmonary disease (COPD) in 4 (21%); followed by hypertension in 3 (15.8%) and diabetes in 3 (15.8%) patients. Overall, of the 97 influenza infected patients, 9 (9.3%) needed hospitalisation to the intensive care unit (ICU); one patient with COPD died due to multi-organ failure. CONCLUSIONS Both the pandemic and seasonal strains were found to be co-circulating in the community. Patients with severe hypoxia, hypertension, acute respiratory distress syndrome and shock required ICU care.

Air pollution exposure assessment simulation of babies in a bike trailer and implication for mitigation measures

Abstract: Young children are a vulnerable population cohort. They receive higher exposure to particulate matter than adults in outdoor roadside environments, necessitating research on an unexplored area of exposure to young children in electric bike trailers. We simulated the exposure profiles of an adult cyclist and young children sitting in a bike-trailer attached to it for multiple air pollutants – particulate matter ≤10µm in aerodynamic diameter (PM10), ≤2.5µm (PM2.5; fine particles), ≤1µm (PM1), BC, and CO2 – during the school run in the morning and afternoon hours. We assessed the differences in their exposure concentrations and analysed the impact of trailer covers and COVID-19 lockdown restrictions via simultaneous measurements under six settings forming three scenarios: (i) bike-trailer versus adult cyclist height; (ii) bike-trailer with and without the cover; and (iii) exposure during the lockdown and eased-lockdown periods. We carried out a total of 82 single runs covering a length of 176 km. These runs were repeated on a 2.1 km long predefined route between an origin (University campus) and destination (a local school) to simulate morning drop-off (08:00-10:00h; local time) and afternoon pick-up (15:00-17:00h) times of school children. Substantial variability was observed in concentrations of measured pollutants within each run (e.g., up to 97% for BC) and between different runs (e.g., ∼93% for PM2.5 during morning versus afternoon) in bike-trailer. Compared with cyclist height, the average bike-trailer concentration of coarse (fine) particles was higher by up to 14% (2.5%) and 18% (14%) during morning and afternoon runs, respectively. The lockdown restrictions when schools were closed led to a reduction in bike-trailer PM2.5 concentrations by up to 91% compared with eased lockdown period when schools re-opened in March 2021. Trailer covers led up to 50% (fine particles) and 24% (BC; a component of PM2.5) reductions in concentrations during the morning runs compared with trailers without cover. Young children carried in bike trailers are exposed to higher air pollution concentrations compared with the cyclist, particularly during peak morning periods at urban pollution hotspots such as traffic lights.