Seasonal Analysis of Particulate Matter Concentrations at a Heavily Trafficked Urban Site

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Prediction of Indoor Air Quality in a School Building Using Risk Model

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Comparative Evaluation of Air Quality Dispersion Models for Predicting Particulate Matter Concentrations at a Busy Traffic Roadway in Chennai City

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Sens-BERT: Enabling Transferability and Re-calibration of Calibration Models for Low-cost Sensors under Reference Measurements Scarcity

Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To overcome these limitations, in this work, we propose Sens-BERT, a BERT-inspired learning approach to calibrate LCS, and it achieves the calibration in two phases: self-supervised pre-training and supervised fine-tuning. In the pre-training phase, we train Sens-BERT with only LCS data (without reference station observations) to learn the data distributional features and produce corresponding embeddings. We then use the Sens-BERT embeddings to learn a calibration model in the fine-tuning phase. Our proposed approach has many advantages over the previous works. Since the Sens-BERT learns the behaviour of the LCS, it can be transferable to any sensor of the same sensing principle without explicitly training on that sensor. It requires only LCS measurements in pre-training to learn the characters of LCS, thus enabling calibration even with a tiny amount of paired data in fine-tuning. We have exhaustively tested our approach with the Community Air Sensor Network (CAIRSENSE) data set, an open repository for LCS.Comment: 1

Covid-19 impact on air quality in megacities

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing Covid-19 pandemic. A particularly drastic measure, the "lockdown", urged people to stay at home and thereby prevent new Covid-19 infections. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during and before the lockdown and compare these to a similar time period from 2019. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis during lockdown). For Delhi, we observe a much stronger decrease of pollution concentrations, including high pollution states. These results could help to design rules to improve long-term air quality in megacities.Comment: 13 pages. Preliminary version of Supplementary Information and open code available here https://osf.io/jfw7n/?view_only=9b1d2320cf2c46a1ad890dff079a2f6

Performance Evaluation of UK ADMS-Urban Model and AERMOD Model to Predict the PM10 Concentration for Different Scenarios at Urban Roads in Chennai, India and Newcastle City, UK

The pollutants and its effects on human health are now a major issue around the world. The impact of traffic and the resulting vehicle emissions has come to the forefront. Particulate matter is one among six criteria pollutants and air pollution related to particulate matter is now becoming a serious problem in developing as well as developed countries. One of the main sources is from the vehicles and the resuspension caused by the vehicular movement. Source apportionment studies of Chennai (Clean Air Asia: Air quality profile 2010 edition) showed that from the residential monitoring stations levels of particulate matter in Chennai lies in the range of 51–70 µg/m3. According to DoT of the total road emissions in UK, about 80 is generated from particulate matter which is due to road traffic even though there are no factors like resuspension in this country. In UK, 103 areas have been declared as local air quality management areas (LAQMA), while in India, 72 cities have been identified as non-attainment area with respect to various air pollutants. Chennai, India and Newcastle City, UK which are the cities under study are the one among them facing severe air pollution problems. The main objective of the paper is application and evaluation of UK ADMS-Urban and AERMOD model for the prediction of particulate matter (PM10) concentrations at urban roadways in Chennai and in Newcastle. The model evaluation has been carried out using traffic data of 2009, meteorological data provided by Laga Systems, Hyderabad for both the cities and the real-time monitored data of the year 2009. The results of the study identified the trends in pollutant patterns and its variation with the different parameters of meteorological data. The statistical descriptors, namely index of agreement (IA), fractional bias (FB), normalized mean square error (NMSE), geometric mean bias (MG) and geometric mean variance (VG) were used to understand the performance of the model. Results indicated that both the models have been able to predict the pollutant concentration with reasonable accuracy. The IA values for ADMS and AERMOD are found to be 0.39 and 0.37, and 0.48 and 0.44, respectively, for Chennai and Newcastle City

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Performance Study Of Thermally Activated Glass Fibre Reinforced Gypsum Roof

Globally, building sector consumes a large amount of energy both for construction and operation. Especially, heating, ventilation, and air conditioning contribute for 40 – 50% of total building energy consumption. Thermally Activated Building System (TABS), which is an energy efficient alternative for conventional mechanical air conditioning, can reduce the energy consumption of building operation. TABS is based on radiant cooling, which also utilizes the thermal mass of the building to achieve thermal comfort of the indoor space. In TABS, chilled water is circulated through the pipes embedded in the building structures. Chilled water removes the heat from the indoor and provides comfort to the occupants. In addition to TABS, use of appropriate building material can enhance the energy-saving potential of buildings not only by reducing the cooling/heating load but also by reducing the embodied energy of the building. Therefore, the building material is also focused in the present study. A sustainable and eco-friendly building material namely Glass Fibre Reinforced Gypsum (GFRG) is integrated with the TABS. The air cavities in the GFRG panels reduce the solar heat penetration through it. In addition, the low thermal conductivity of GFRG reduces the heat transfer from outdoor to indoor. Thus, reduction in cooling load and therefore enhancement in energy saving can be realized. The combination of TABS and GFRG is named as Thermally Activated Glass Fibre Reinforced Gypsum (TAGFRG). The present study aims to analyze the impact of various design and operating parameters on the performance of TAGFRG roof. The roof has copper pipes embedded in the bottom half of the panel cavities, whereas the top half of the cavities have air gaps. A commercial CFD tool has been used to simulate the TAGFRG. The design and operating parameters analyzed are the diameter, wall thickness and thermal conductivity of pipe, pipe spacing, and temperature and flow rate of supply water. The results conclude that the temperature of roof bottom surface decreases with the increase in diameter and thermal conductivity of pipe, and decrease in wall thickness and spacing of pipes. The average bottom surface temperature of the roof is 31.9°C for no cooling case. This reduces to 20°C for the supply water temperature of 16°C with the average heat removal of 120 W/m2from the space by the bottom roof surface