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SAW devices may be configured as strain sensors, providing passive, wireless strain measurement in demanding conditions. A key consideration is the modeling of the sensors, enabling different device designs to be considered. This paper presents a simulation scheme using coupling-of-modes (COM) analysis which allows both the frequency response of a SAW strain sensor and its bias sensitivity to be evaluated. Example applications are presented to demonstrate the use of the model. [ABSTRACT FROM AUTHOR]

Air pollution from transport hubs is a recognised health concern for local urban inhabitants. Within the domain of transport hubs, significant attention has been given to larger airport and port settings, however concerns have been raised about emissions from urban railway hubs, especially those with diesel trains. This paper presents an approach that adopts low-cost monitoring (LCM) for fixed site monitoring (FSM) to quantify and disaggregate PM 2.5 and NO 2 contributions from railway station and road traffic on air quality in the vicinity of railway station in Dublin, Ireland. The NO 2 sensor showed larger discrepancies than the PM 2.5 sensor when compared to the reference monitor. Machine learning models (XGBoost and Random Forest (RF) regression) were applied to calibrate the LCM devices, with the XGBoost model (NO 2, R 2  = 0.8 and RSME = 9.1 μg/m 3 & PM 2.5 , R 2  = 0.92 and RSME = 2.2 μg/m 3 ) deemed more appropriate than the RF model. Local wind conditions, pressure, PM 2.5 concentrations, and road traffic significantly impacted NO 2 model results, while raw PM 2.5 sensor readings greatly influenced the PM 2.5 model output. This highlights that the NO 2 sensor requires more input data for accurate calibration, unlike the PM 2.5 sensor. The monitoring results from the one-month monitoring campaign from May 25, 2023 to June 25, 2023 presented elevated NO 2 and PM 2.5 concentrations measured at the railway station, which translated to exceedances of the annual WHO limits (PM 2.5  = 5 μg/m 3 , NO 2  = 10 μg/m 3 ) by 1.6-1.8 and 3.2-5.2 times respectively at the study site. A subsequent data filtering technique based on wind orientation, revealed that the railway station was the main PM 2.5 source and road traffic was the main NO 2 source when winds come from the railway station. This study highlights the value of LCM devices alongside robust machine learning techniques to capture air quality in urban settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shanmuga Priyan reports financial support was provided by Environmental Protection Agency. Yuxuan Guo reports financial support was provided by Environmental Protection Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Links between air pollution and asthma are less well established for older adults than some younger groups. Nitrogen dioxide (NO2) concentrations are widely used as an indicator of transport-related air pollution, and some literature suggests NO2 may directly affect asthma., Methods: This study used data on 8162 adults >50 years old in the Republic of Ireland to model associations between estimated annual outdoor concentration of NO2 and the probability of having asthma. Individual-level geo-coded survey data from The Irish Longitudinal Study on Ageing (TILDA) were linked to model-based estimates of annual average NO2 at 50 m resolution. Asthma was identified using two methods: self-reported diagnoses and respondents' use of medications related to obstructive airway diseases. Logistic regressions were used to model the relationships., Results: NO2 concentrations were positively associated with the probability of asthma [marginal effect (ME) per 1 ppb of airborne NO2 = 0.24 percentage points asthma self-report, 95% confidence interval (CI) 0.06-0.42, mean asthma prevalence 0.09; for use of relevant medications ME = 0.21 percentage points, 95% CI 0.049-0.37, mean prevalence 0.069]. Results were robust to varying model specification and time period. Respondents in the top fifth percentile of NO2 exposure had a larger effect size but also greater standard error (ME = 2.4 percentage points asthma self-report, 95% CI -0. 49 to 5.3)., Conclusions: Associations between local air pollution and asthma among older adults were found at relatively low concentrations. To illustrate this, the marginal effect of an increase in annual average NO2 concentration from sample minimum to median (2.5 ppb) represented about 7-8% of the sample average prevalence of asthma., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

SAW devices may be configured as strain sensors, providing passive, wireless strain measurement in demanding conditions. A key consideration is the modeling of the sensors, enabling different device designs to be considered. This paper presents a simulation scheme using coupling-of-modes (COM) analysis which allows both the frequency response of a SAW strain sensor and its bias sensitivity to be evaluated. Example applications are presented to demonstrate the use of the model.