Mobile Monitoring Research Articles

Making it work for all: equity in single ventricle monitoring.

Interstage monitoring programs for single ventricle disease have been developed to reduce morbidity and mortality. There is increased use of telemedicine and mobile application monitoring. It is unknown if there are disparities in use based on patient socio-demographic factors. We conducted a retrospective cohort study of patients enrolled in the single ventricle monitoring program and KidsHeart application at a single centre from 4/21/2021 to 12/31/2023. We investigated the association of socio-demographic factors with telemedicine usage, mobile application enrollment and usage. We assessed resource utilisation and weight changes by program era. There were 94 children in the cohort. Patients with Norwood and ductal stent had higher mean telemedicine visits per month (1.8 visits, p = 0.004), without differences based on socio-demographic factors. There were differences in application enrollment with more Black patients enrolled compared to White patients (p = 0.016). There were less Hispanic patients enrolled than Non-Hispanic patients (p = 0.034). There were no Spaish speaking patient's enrolled (p = 0.0015). There were no patients with maternal education of less than high school enrolled and all those with maternal education of advanced degree were enrolled (p = 0.0016). There was decreased mobile application use in those from neighbourhoods in the lowest income quartile. There were decreased emergency department visits with mobile application monitoring. Mean weight-for-age z-scores had increased from start to completion of the program in all eras. Differences were seen in mobile application enrollment and usage based on socio-demographic factors. Further work is needed to ensure that all patients have access to mobile application usage.

Modeling Urban Temperature Using Measurements from Mobile and Stationary Monitoring Stations

Heat waves are occurring more frequently worldwide as global warming continues, and urban heat islands can threaten conventional life in cities. Measuring, analyzing, and simulating weather data at fine spatial and temporal scales are essential to prevent and reduce the damage caused by extreme heat waves. In urban environments, handling complex micrometeorological situations using current meteorological stations and global simulation models (e.g., weather research forecasting models) is challenging. In this study, the thermal environments of urban areas were measured using a mobile meteorological measurement platform. Both mobile and stationary datasets were incorporated into the meteorological modeling process to simulate the spatial and temporal distribution of temperature. Additionally, various mobile observation implementation scenarios for temperature modeling were examined. We compared simulation combinations with the temperature field generated from the total dataset to obtain a better sampling campaign and properly incorporate mobile data scenarios. When collecting mobile data, it is important to consider spatial features to improve the efficiency of sampling programs. This can substantially reduce the cost of mobile data collection, together with the sensor error bound.

Mobile health monitoring of children with CHDs.

Mobile health has been shown to improve quality, access, and efficiency of health care in select populations. We sought to evaluate the benefits of mobile health monitoring using the KidsHeart app in an infant CHD population. We reviewed data submitted to KidsHeart from parents of infants discharged following intervention for high-risk CHD lesions including subjects status post stage 1 single ventricle palliation, ductal stent or surgical shunt, pulmonary artery band, or right ventricular outflow tract stent. We report on the benefits of a novel mobile health red flag scoring system, mobile health growth/feed tracking, and longitudinal neurodevelopmental outcomes tracking. A total of 69 CHD subjects (63% male, 41% non-white, median age 28 days [interquartile range 20, 75 days]) were included with median mobile health follow-up of 137 days (56, 190). During the analytic window, subjects submitted 5700 mobile health red flag notifications including 245 violations (mean [standard deviation] 3 ± 3.96 per participant) with 80% (55/69) of subjects submitting at least one violation. Violations precipitated 116 interventions including hospital admission in 34 (29%) with trans-catheter evaluation in 15 (13%) of those. Growth data (n = 2543 daily weights) were submitted by 63/69 (91%) subjects and precipitated 31 feed changes in 23 participants. Sixty-eight percent of subjects with age >2 months submitted at least one complete neurodevelopment questionnaire. In our initial experience, mobile health monitoring using the KidsHeart app enhanced interstage monitoring permitting earlier intervention, allowed for remote tracking of growth feeding, and provided a means for tracking longitudinal neurodevelopmental outcomes.

Diffusion and Exchange Kinetics of Microparticle Formulations by Spatial Fourier Transform Fluorescence Recovery after Photobleaching with Patterned Illumination.

The mechanism of active pharmaceutical ingredient (API) mobility during release in microparticle formulation was investigated using periodically structured illumination combined with spatial Fourier transform fluorescence recovery after photobleaching (FT-FRAP). FT-FRAP applies structured photobleaching across a given field of view, allowing for the monitoring of molecular mobility through the analysis of recovery patterns in the FT domain. Encoding molecular mobility in the FT domain offers several advantages, including improved signal-to-noise ratio, simplified mathematical calculations, reduced sampling requirements, compatibility with multiphoton microscopy for imaging API molecules within the formulations, and the ability to distinguish between exchange and diffusion processes. To prepare microparticles for FT-FRAP analysis, a homogeneous mixture of dipyridamole and pH-independent methyl methacrylate polymer (Eudragit RS and RL) was processed using laminar jet breakup induced by vibration in a frequency-driven encapsulator. The encapsulated microparticles were characterized based on particle size distribution, encapsulation efficiency, batch size, and morphology. Utilizing FT-FRAP, the internal diffusion and exchange molecular mobility within RL and RS microparticles were discriminated and quantified. Theoretical modeling of exchange- and diffusion-controlled release revealed that both RL and RS microparticles exhibited similar exchange decay rates, but RL displayed a significantly higher diffusion coefficient. This difference in diffusion within RL and RS microparticles was correlated with their macroscopic dissolution performance.

A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas

National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.

Trust and distrust coexistence and change in the adoption of an emerging technology in organizations

Emerging technologies challenge organizations by introducing information systems that transform processes and introduce a series of risks and uncertainties for stakeholders. These conditions make trust and distrust important in the adoption and implementation of technology. Most of the trust in Information Systems literature has focused on building the constructs of trust and distrust between an artifact and the final user, but there is a gap in the analysis of changes in trust and distrust beliefs and their possible coexistence across time. This case study illustrates how trust and distrust manifest across a mobile health monitoring system implementation: the attitudes, objects of trust/distrust, and the changes they exhibit across four stages. The results point at the objects of trust/distrust corresponding to the technology frames that shift during the implementation from an artifact-centric to a more encompassing technology involving other users, programmers, and themselves. The actual technology use leads to changes in perceived risks and expectations, which are fed by the interaction of all users with the system. Behaviors are shaped by coexisting trust and distrust in technology beliefs, which usually compensate for the elements of the technology they distrust. The study concludes that the coexistence of trust and distrust in technology is a constantly shifting phenomenon, leading to a complex yet stabilized use of technology.

In-situ online investigation of biogenic volatile organic compounds emissions from tropical rainforests in Hainan, China

Biogenic volatile organic compounds (BVOCs) emitted by tropical plants represent a significant proportion of global emissions, but the in-situ BVOC measurements in tropical rainforests are extremely sparse. Herein, a vehicle-mounted mobile monitoring system was developed for in-situ online investigations of BVOC emissions from thirty representative tree species in the tropical rainforests of Hainan Island, southern China. The results showed that monoterpenes were the primary BVOCs emitted from most broadleaf trees. Isoprene, sabinene, γ-terpinene and β-ocimene were the most abundant BVOCs. Localized canopy-scale emission factors (EFs) exhibited notable discrepancies with the defaults in the Model of Emissions of Gases and Aerosols from Nature (MEGAN), specifically with isoprene EFs being slightly lower than the model, while the EFs for monoterpenes and sesquiterpenes were 1 to 27 times higher than those in MEGAN. The BVOC emission inventory for the predominant mature forest species in Hainan Island in 2023 was further estimated to be 244.43 Gg C·yr−1, with isoprene and monoterpenes accounting for 74 % and 16 %, respectively. Additionally, unimodal monthly variation patterns were revealed, with BVOC emissions peaked in July (30.08 Gg C·yr−1) and bottomed in January (8.84 Gg C·yr−1). This study demonstrates the potential and versatility of the applied mobile platform for in-situ online measurements of plant volatiles. Our findings provide important data support for reducing uncertainties in BVOC emission estimations in tropical rainforests and for evaluating their health benefits in the context of forest therapy.

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements. It was found that regional sources of pollution were mostly responsible for the PM2.5 and PM1 concentrations. In contrast, PM10 was mostly associated with local sources. The total particle number and the lung deposited surface area of PM were almost solely associated with traffic, while black carbon was associated with both the sources from the urban background and local traffic. Our analysis showed that while the effect of the hyperlocal sources, such as emissions from construction works or traffic, do not exceed the distance of a couple of hundred meters, they can influence the health of thousands of people in densely populated areas. Thus, using this novel approach we illustrate the limitations of the present measurement network paradigm and offer an alternative and versatile approach to understanding the hyperlocal factors that affect urban air quality. Mobile monitoring by citizen scientists is shown to have huge potential to enhance spatiotemporal resolution of air quality data without the need of extensive and expensive campaigns.

Mobile monitoring of air pollutant concentration in the park of Urumqi, China

The concentrations of PM2.5, PM10, O3 and NO2 in Urumqi People's Park were monitored by mobile monitoring in July and October 2023, and the temporal and spatial changes of pollutant concentrations in urban parks and their influencing factors were analyzed. The results show that park can effectively reduce PM and NO2 concentration, increase O3 concentration. PM concentration in the park was higher in the morning than the noon. O3 and NO2 concentration is lower in the morning than the noon. The concentration within the park was lower than that outside the park. The mitigative effect of pollutants in the park was better in summer than in autumn. The concentration of PM in the park showed a high value aggregation, and PM2.5 and PM10 showed the same spatial distribution in the high value cluster, and the pollution of both showed homology in summer. O3 and NO2 concentrations tend to accumulate at low value cluster. Pollutants are affected by the local environment. PM2.5 concentration is greatly affected by wind speed and distance from water, O3 concentration is greatly affected by temperature and average distance from road. The closer to the road, the higher the concentration of NO2. Attention should be paid to the landscape design of the buffer zone of 30–50 m with the high pollution area. This study can provide reference for park planning and design.

Understanding health literacy's moderating role in remote monitoring for adjuvant endocrine therapy: Findings from a randomized clinical trial.

45 Background: Despite the efficacy of oral adjuvant endocrine therapy (AET) for improving survival among women with hormone receptor-positive early-stage breast cancer, adherence rates remain low. Our THRIVE study (NCT03592771) investigated the effectiveness of a mobile health remote monitoring app with and without tailored educational messages on AET adherence among women with breast cancer and found no statistically significant treatment effects on 1-year AET adherence overall. Health literacy contributes to patients’ understanding of their treatment plans. We conducted a post hoc analysis of the THRIVE study to examine if health literacy moderates the effectiveness of this intervention. Methods: This non-blinded randomized controlled trial included women with early-stage breast cancer prescribed AET at a large cancer center with 14 clinics across three states. Participants used a pillbox to electronically monitor AET adherence for 1 year and completed surveys at enrollment. Consented participants were randomized into (1) “App”, receiving access to the study adherence and symptom monitoring app for 6 months, with increasing/severe symptoms and missed doses reported in the app triggering follow-ups from the oncology team; (2) “App+Feedback”, receiving additional weekly text messages about managing symptoms, adherence, and communication for 6 months; or (3) “Enhanced Usual Care (EUC).” The primary outcome was 1-year AET adherence captured with the pillbox (≥80% Proportion of Days Covered [PDC] vs. < 80%). The enrollment survey captured participant’s sociodemographic characteristics, including race and ethnicity, education, household income, and health literacy. We used a linear probability model to measure the interaction between the study arm and health literacy on AET adherence. Results: Among 304 women randomized (104 EUC, 98 App, and 102 App+Feedback), the 12-month follow-up retention rate was 88% (n = 266) and 19.4% reported low health literacy at enrollment. Low health literacy was more prevalent among Black (29.4%) vs. White (13.4%) participants (p < 0.001), those with incomes below the federal poverty level (34.4% vs. 17.2% of those with higher incomes, p = 0.02), and those with only a high school degree or lower education (31.7%) vs. with those with some college or higher education (16.4%, p < 0.01). In the low health literacy group, 80.0% of App+Feedback were AET adherent vs. 42.1% of EUC, a 37.9 percentage point (ppt) difference (95% CI: 4.1 to 71.7, p = 0.03); in higher literacy group adherence, 47.1% of App+Feedback vs. 59.0% of EUC were adherent, a -11.8 ppt difference (95% CI: -27.9 to 4.3, p = 0.15). Conclusions: A remote monitoring app with tailored educational text messages led to higher 1-year AET adherence among participants with low health literacy, but not for those with high health literacy. Clinical trial information: NCT03592771 .

Transforming in-clinic post-operative and intermediate care with cosinuss°

Continuous, mobile patient monitoring plays a critical role in healthcare, particularly for post-surgery, intermediate care in clinics. The implementation of vital signs monitoring technology enables healthcare professionals to triage patients effectively by maintaining real-time awareness of their health status and allowing for prompt intervention when necessary. This technology supports early mobilization and facilitates the detection of potential complications such as post-surgical sepsis. cosinuss° technology has been evaluated in various studies, in terms of its accuracy in capturing vital parameters and its usability, emphasizing its potential to enhance intermediate patient care and outcomes. This report outlines the design and implementation of cosinuss° Health patient monitoring solution for use in intermediate, postoperative clinic settings. It presents the results and insights from three recent, in-clinic applications, discussing both technical and practical aspects, clinical processes, and the reported satisfaction from both patients and medical caregivers. The findings highlight the promising potential of cosinuss° Health on improving patient monitoring and overall clinical outcomes.

Optimisation of bioaerosol sampling using an ultralight aircraft: A novel approach in determining the 3-D atmospheric biodiversity

Bioaerosols, such as pollen and fungal spores, are routinely monitored for agricultural, medical or urban greening practices, but sampling methodology is largely relying on techniques more than half a century old. Moreover, biomonitoring campaigns often take place in urban environments, although sources can be located outside cities’ borders with ampler vegetation. Therefore, the question arises whether we are accurately picturing the biodiversity and abundance of regional bioaerosols and whether those locally detected might derive from long-distance transport, horizontally or vertically. To answer the above, we used novel, mobile monitoring devices, and aerial measurement units, like aircrafts, so as to explore bioaerosol concentrations at a variety of altitudes.An ultralight aircraft was equipped with a sampling device for bioaerosols. The device consisted of duplicate isokinetic impactors that match the physical functioning and the microscopic quantification method of the widely used ground-based Hirst-type impactors. Isokinetic airflow was realized by adjusting the air flux at the impactors’ inlet to the airspeed of the aircraft. Three campaigns were made, where the comparability, efficiency and accuracy of different sampling devices were determined, namely of the abovementioned impactor, and of the mobile conventional Hirst-type pollen sampler. The campaigns involved measurements from ground level (0 m altitude) up to 900 m (above ground level (agl)) via flights.Our results showed that aircraft-based airborne pollen concentration measurements were consistently higher than those of all other devices, regardless of the altitude and sampling time. It is noteworthy that the pollen concentration exceeded 500 pollen grains/m3 at >900 m of altitude, this concentration being 1.77 times higher than that simultaneously measured at ground level. Likewise, the diversity of pollen was also higher at higher altitude.Our results indicate the usability and superiority of small aircraft and high-flow impactors for research, achieving higher biodiversity and abundance over a shorter sampling interval compared to conventional volumetric techniques. Higher pollen amounts at higher altitudes also point at the necessity to monitor bioaerosols across the vertical dimension, especially in densely populated areas and high-traffic air space.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California’s diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989–2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively.Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations.To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)–NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions.Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

ANALYSIS AND ASSESSMENT OF AIR QUALITY IN ASTANA: COMPARISON OF POLLUTANT LEVELS AND THEIR IMPACT ON HEALTH

This study presents an in-depth analysis of air quality in Astana, Kazakhstan, utilizing both mobile and stationary air monitoring systems over a two-year period. The research focuses on tracking key air pollutants, namely carbon monoxide (CO), nitrogen dioxide (NO₂), particulate matter (PM2.5 and PM10), and sulfur dioxide (SO₂), providing a comparative assessment of seasonal trends and the sources of pollution, which include transportation, industrial emissions, and domestic heating during the cold season. The study emphasizes the significance of monitoring systems in urban environments to understand better the impact of air pollution on public health and the effectiveness of sustainable interventions. One of the major insights from this research is the comparison between seasonal variations in pollutant levels and the city's transition toward sustainable energy practices, such as increased gasification and the use of electric transportation, which has already demonstrated a positive impact on reducing emissions during peak heating periods. The results show that while Astana has improved air quality, air pollution remains a concern, especially in winter due to the increased use of solid fuel. This paper emphasizes the importance of real-time data from mobile sensors and suggests their wider use to complement stationary sensors for better monitoring. In addition to pollutant tracking, the study delves into the health implications of prolonged exposure to air pollutants, particularly in urban areas. The study concludes by advocating for expanded use of mobile monitoring systems and advanced data analytics to provide actionable insights for policymakers, urban planners, and public health officials.

Engaging Social Leadership Character on Cadets Training during Onboard Based on Mobile Application Monitoring: A Preliminary Study

This preliminary study delves into the effectiveness of mobile application monitoring in enhancing social leadership character among cadets during onboard training. The research focuses on the social dimension of character development, which encompasses the ability to act with proper decorum in all professional, social, and online environments. The study explores how mobile technology can facilitate experiential learning, teamwork, and leadership skills, improving cadets' social interactions and leadership abilities. The mixed-methods approach employed in this study combines quantitative data from mobile application usage with qualitative insights from cadet feedback and mentorship programs. This comprehensive methodology allows for a deeper understanding of the impact of mobile applications on cadets' social leadership development. The findings indicate that mobile applications can significantly enhance cadets' engagement in social leadership activities, improve their teamwork and problem-solving skills, and provide real-time feedback for character development. Moreover, the study highlights the potential of mobile technology to support comprehensive character development in cadet training programs. By leveraging mobile applications, training programs can create a more immersive and interactive learning environment that fosters the development of essential social leadership skills. The results of this study have significant implications for educational institutions and military academies seeking to enhance their cadet training programs and prepare future leaders with robust social leadership capabilities. Overall, this preliminary study underscores the importance of integrating mobile technology into cadet training to promote social leadership character development, thereby equipping future leaders with the skills to navigate complex social environments effectively.

Spatial distribution of air pollutants in different urban functional zones based on mobile monitoring and CFD simulation

Spatial distribution of air pollutants in different urban functional zones based on mobile monitoring and CFD simulation

Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study.

Older adults discharged from the emergency department (ED) face elevated risk of falls and functional decline. Smartphones might enable remote monitoring of mobility after ED discharge, yet their application in this context remains underexplored. This study aimed to assess the feasibility of having older adults provide weekly accelerometer data from an instrumented Timed Up-and-Go (TUG) test over an 11-week period after ED discharge. This single-center, prospective, observational, cohort study recruited patients aged 60 years and older from an academic ED. Participants downloaded the GaitMate app to their iPhones that recorded accelerometer data during 11 weekly at-home TUG tests. We measured adherence to TUG test completion, quality of transmitted accelerometer data, and participants' perceptions of the app's usability and safety. Of the 617 approached patients, 149 (24.1%) consented to participate, and of these 149 participants, 9 (6%) dropped out. Overall, participants completed 55.6% (912/1639) of TUG tests. Data quality was optimal in 31.1% (508/1639) of TUG tests. At 3-month follow-up, 83.2% (99/119) of respondents found the app easy to use, and 95% (114/120) felt safe performing the tasks at home. Barriers to adherence included the need for assistance, technical issues with the app, and forgetfulness. The study demonstrates moderate adherence yet high usability and safety for the use of smartphone TUG tests to monitor mobility among older adults after ED discharge. Incomplete TUG test data were common, reflecting challenges in the collection of high-quality longitudinal mobility data in older adults. Identified barriers highlight the need for improvements in user engagement and technology design.

Design and Development of Mobile Based Application for Competency Oriented Dental Education

Abstract Background: Teaching, Learning and Asssessment in Competency Based Dental Education need to be digitally captured in an user friendly manner which can be accessed by students and mentor which would be useful for continuous monitoring, periodic assessment and maintenance of a digital record. Methods: A novel customised mobile based application MSCORE© developed for competency oriented dental training among undergraduate interns of an institute. Initial perception on the use of the application was recorded using a questionnaire from the entire batch of interns. Factor Analysis was done to check the validity and reliability of the questionnaire. Results: From the 30-item questionnaire, factor analysis extracted four aforementioned factors and reliability was found for 22 questions. Sixty-three percent of students felt that the application is easy to learn. Overall high scores were recorded for 71% of the students. Eighty-five percent of the students scored high on the suitability of the task. Seventy three scored the high on the satisfaction scale. However, advantages over existing written methods scored high only among 49% of the students. Conclusions: Mobile application-based mentoring, monitoring and assessment can be a better alternative to conventional models as it was found to demonstrate good perception by the digital natives of the present generation.

Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0–40.6% and 20.8–42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of “hot” spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin.

Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science.

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68-0.9, Uexp = 16-66%, BSU = 0.1-0.7 µg/m3) and BC (R2 = 0.82-0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38-0.55, Uexp = 111-614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75-0.83, Uexp = 37-44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.