Mobile Campaigns Research Articles

Impact of airport operations and road traffic on the particle number concentration in the vicinity of a suburban airport

The impact of airports on ambient air pollution is a major concern due to its impact on public health. This study analyzes the sub-micron total particle number concentration (PNC) as a proxy for ultrafine particles in the immediate vicinity of Berlin-Tegel Airport (TXL) based on a mobile measurement campaign in summer 2019. With predominantly westerly winds, 45 measurement runs took place along a 20–30 km route to the east of the airport. The highlights of the study are as follows: 1. Berlin-Tegel Airport had a distinct but a spatially limited impact on the residential areas to the east of the airport. 2. Particle number concentrations in the lee of the airport are significantly higher than the mean of the entire area. 3. Locations along the eastward extension of the runways are significantly more affected than those outside the approach corridor. 4. The impact of airport operations on PNC in the adjacent neighborhood is comparable to the combined impact of busy roads in the area. The closure of Berlin-Tegel Airport at the end of 2020 should have considerably improved the air quality in the residential areas in the close vicinity of the airport.

A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements.

Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO2) and ultrafine particles (UFP) collected by Google Street View cars with long-term near-road measurements from regular monitoring networks in Amsterdam. We found that transfer-learning LUR models performed 55.2% better in predicting long-term near-road concentrations than the LUR model trained only with mobile measurements for NO2 and 26.9% for UFP, evaluated by normalized mean absolute errors. This improvement in model accuracy suggests that transfer-learning models provide a solution for narrowing the knowledge gaps and can improve the accuracy of mapping long-term near-road air pollution concentrations using short-term on-road mobile monitoring data.

HIV Incidence in Botswana Rural Communities With High Antiretroviral Treatment Coverage: Results From the Botswana Combination Prevention Project, 2013-2017.

The Botswana Combination Prevention Project demonstrated a 30% reduction in community HIV incidence through expanded HIV testing, enhanced linkage to care, and universal antiretroviral treatment and exceeded the Joint United Nations Programme on HIV/AIDS 90-90-90 targets. We report rates and characteristics of incident HIV infections. The Botswana Combination Prevention Project was a community-randomized controlled trial conducted in 30 rural/periurban Botswana communities from 2013 to 2017. Home-based and mobile HIV-testing campaigns were conducted in 15 intervention communities, with 39% of participants testing at least twice. We assessed the HIV incidence rate [IR; number of new HIV infections per 100 person-years (py) at risk] among repeat testers and risk factors with a Cox proportional hazards regression model. During 27,517 py, 195 (women: 79%) of 18,597 became HIV-infected (0.71/100 py). Women had a higher IR (1.01/100 py; 95% confidence interval: 0.99 to 1.02) than men (0.34/100 py; 95% confidence interval: 0.33 to 0.35). The highest IRs were among women aged 16-24 years (1.87/100 py) and men aged 25-34 years (0.56/100 py). The lowest IRs were among those aged 35-64 years (women: 0.41/100 py; men: 0.20/100 py). The hazard of incident infection was the highest among women aged 16-24 years (hazard ratio = 7.05). Sex and age were significantly associated with incidence (both P < 0.0001). Despite an overall reduction in HIV incidence and approaching the United Nations Programme on HIV/AIDS 95-95-95 targets, high HIV incidence was observed in adolescent girls and young women. These findings highlight the need for additional prevention services (pre-exposure prophylaxis and DREAMS) to achieve epidemic control in this subpopulation and increased efforts with men with undiagnosed HIV.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

A Deep Learning-Based Decision Support System for Mobile Performance Marketing

In Mobile Performance Marketing (MPM), monetary compensation only occurs when an advertisement results in a conversion (e.g., sale of a product or service). In this work, we propose an intelligent decision support system (IDSS) to automatically select mobile marketing campaigns for users. The IDSS is based on a computationally efficient mobile user conversion prediction model that assumes a novel Percentage Categorical Pruning (PCP) categorical preprocessing and an online deep multilayer perceptron (MLP) reuse model (MLPr). Using private (nonpublicly available) business MPM data provided by a marketing company, the MLPr model outperformed an offline multilayer perceptron and a logistic regression, obtaining a high quality class discrimination when applied to sampled (85% to 92%) and complete (90% to 94%) data. In addition, the MLPr compared favorably with other machine learning (ML) models (e.g., Random Forest, XGBoost), as well as with other deep neural networks (e.g., diamond shaped). Moreover, we designed two strategies (A — best campaign selection; and B — random selection among the top candidate campaigns) to build the IDSS, in which the predictive deep learning model is used to perform a real-time selection of advertisement campaigns for mobile users. Using recently collected big data (with millions of redirect events) from a worldwide MPM company, we performed a realistic IDSS evaluation that considered three criteria: response time, potential profit and advertiser diversity. Overall, competitive results were achieved by the IDSS B strategy when compared with the current marketing company ad assignment method.

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements.

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R2 = 0.66 and RMSE = 9391.8#/cm3 (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.

A comprehensive methodology for assessing outdoor thermal comfort in touristic city of Porto (Portugal)

Urban tourism's impact on intra-urban temperature differences and the latter's influence on thermal comfort in public space, in a context of climate change, is still not clear. When it comes to urban tourism, few are the studies which assess human thermal comfort in the form of visual and cartographic representation. This paper seeks to fill this gap by obtaining data through measurement campaigns carried out in a motor vehicle during the summers and winters of 2019 and 2020, in a total of 27 nights and 7 afternoons. The Portuguese city of Porto was the selected destination. It revealed very useful in identifying the intra-urban temperature differences and the patterns of human thermal comfort in different days of the year. Based on the mobile measurement campaign carried out in the aforementioned period, the main thermal patterns and implications to thermal comfort in the central area of Porto were summarized. There is a multiplicity of situations of thermal comfort and spatial variations of discomfort due to cold and heat stress. These conditions are particularly relevant during the summer season, when the urban heat island intensity can exceed 3.0 °C. The proposed methodology could be very useful for researchers in the field of tourism climatology and policymakers, given that it contributes with adaptive measures regarding the tourism sector in the face of intense vulnerability to climate change.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign.

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Early Impact of SARS-CoV-2 Pandemic on Immunization Services in Nigeria.

Background: By 11 March 2022, there were 450,229,635 coronavirus disease (COVID-19) cases and 6,019,085 deaths globally, with Nigeria reporting 254,637 cases and 3142 deaths. One of the essential healthcare services that have been impacted by the pandemic is routine childhood immunization. According to the 2018 National Demographic and Health Survey, only 31% of children aged 12–23 months were fully vaccinated in Nigeria, and 19% of eligible children in the country had not received any vaccination. A further decline in coverage due to the pandemic can significantly increase the risk of vaccine-preventable-disease outbreaks among children in Nigeria. To mitigate such an occurrence, it is imperative to urgently identify how the pandemic and the response strategies have affected vaccination services, hence, the goal of the study. Methods: The research method was qualitative, including in-depth interviews of healthcare workers and focus group discussions (FGDs) with caregivers of children aged 0–23 months. We selected one state from each of the three zones of Nigeria: northern, central, and southern. Within each state, 10 local government areas and 20 healthcare facilities were purposively selected. In each facility, 10 healthcare workers were invited for interviews. Overall, 517 healthcare workers were interviewed. For the focus group discussion, 30 communities were selected. Within each selected community, six consenting caregivers were included. Overall, 180 caregivers participated. The data were analyzed using thematic inductive content analysis. Results: Three significant impacts that were observed are: difficulties in accessibility to immunization services, declining immunization demand and uptake among caregivers due to varying factors, and erosion of vaccine confidence among both caregivers and healthcare workers. Movement restriction and lockdown had numerous major impacts, such as decreased general healthcare service delivery, increased transportation costs, fewer engagements that promote vaccine uptake, and cessation of mobile vaccination campaigns that target hard-to-reach communities. Moreover, misinformation, conspiracy beliefs about the pandemic and COVID-19 vaccines, and risk perception negatively influenced general vaccine confidence. Conclusion: The results of this early impact study show that immunization was directly affected by the pandemic and provide insights into areas where interventions are needed for recovery.

Take it on the Chin! Advertising Acceptance on Mobile Platforms- A Review of Literature

This study aims to consolidate existing literature results through a systematic review to develop a comprehensive conceptual framework. This study followed Pollock and Berge's (2018) six-stage methods of the systematic review to achieve the aims. Bibliometric and content analysis was conducted using 48 articles collected from six databases: Science Direct, Emerald Insight, Wiley online Library, Sage Pub, Springer Link, and Taylor &amp; Francis. The content analysis reveals three major categories of the antecedent: advert attributes, personal factors, and environmental factors that affect mobile advertising value, attitude, and acceptance. The differences in the factors leading to mobile advertising acceptance are observed across different age groups, regions, advertising types, and cultures. The study concludes with theoretical implications and future research directions. The study highlights some of the regions and mobile advertising contexts where further research is needed. These consolidated findings are useful for advertisers in planning future mobile advertising campaigns.

Determining Inaccurate Coordinates in Electronic Data Collection for Surveillance and Immunization Supportive Supervision: A Case Study of Nigeria EPI Supportive Supervision Module.

The mobile phone global positioning system (GPS) is used to reconnaissance a mobile phone user's location, e.g., at work, home, shops, etc. Such information can be used to feed data gathering expeditions, the actual position of the interviewer/surveyor using the mobile phone inert settings of location mode via GPS, WIFI, and Mobile networks. Mobile devices are becoming progressively erudite and now integrate diverse and robust sensors. The new generation of smartphones is multi-laden with sensors, including GPS sensors. The study describes and evaluates a data-gathering process used by the World Health Organization (WHO–Nigeria, EPI Program) that uses phone-based in-built GPS sensors to identify the position of users while they undergo supportive supervision. This form of spatial data is collected intrinsically using the Open Data Kit (ODK) GPS interface, which interlaces with the mobile phone GPS sensor to fetch the geo-coordinates during the process. It represents a step in building a methodology of matching places on the map with the geo-coordinates received from the mobile phones to investigate deviation patterns by devices and location mode. The empirical results can help us to understand the variation in geospatial data collation across devices and highlight critical criteria for choosing mobile phones for mobile surveys and data campaigns. This study reviewed the existing data gathered inadvertently from 10 brands of smartphones over 1 year of using the mobile data collection with over 80,000 field visits to predict the deviation pattern for spatial data acquisition via mobile phones by different brands.

Investigation of spaceborne trace gas products over St Petersburg and Yekaterinburg, Russia, by using COllaborative Column Carbon Observing Network (COCCON) observations

Abstract. This work employs ground- and space-based observations, together with model data, to study columnar abundances of atmospheric trace gases (XH2O, XCO2, XCH4 and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitudes are much sparser than at low or mid latitudes, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous Copernicus Atmosphere Monitoring Service (CAMS) model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg. The gradients between the two study sites (ΔXgas) are similar between CAMS and CAMS-COCCON datasets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite ΔXgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St Petersburg, the downwind COCCON station measured obvious enhancements for both XCH4 (10.6 ppb) and XCO (9.5 ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4 ppb for XCH4 and 12.5 ppb for XCO.

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

A strategic framework for non-intrusive mobile marketing campaigns

Intrusive marketing is invasive in nature and it has negative impact on the consumers. Due to its turning off effect, customers may develop an antipathy to the brand. Hence, the marketing campaign may lead to just opposite of its objectives. With the large-scale proliferation of mobile communication, there has been a stringent shift from the traditional marketing to mobile phone-based marketing. However, due to the more personalised nature of device, customers are generally more sensitive towards the marketing campaigns delivered on their mobile phones. Any unwanted marketing campaign may elicit negative response and marketers need to be very careful in drafting their strategies to identify what and how to reduce the intrusive nature of their advertisements. The existing frameworks and models for marketing campaigns do not cover the intrusiveness of mobile marketing campaigns. Present work identifies the factors leading to the non-intrusive campaigns and their possible consumer effects. A strategic framework has been proposed for the development of non-intrusive mobile marketing campaigns along with technology-based solutions and content specific suggestions.

A strategic framework for non-intrusive mobile marketing campaigns

A strategic framework for non-intrusive mobile marketing campaigns

Barriers and Motivators for In-house Blood Donation: A Cross-sectional Study from Tertiary Care Hospital, Kilinochchi, Sri Lanka

Introduction: Most Sri Lankan blood donations (93%) are from mobile blood donation campaigns. Restricted community gathering during the Coronavirus Disease-2019 (COVID-19) pandemic has adversely affected the blood supply. Further, Kilinochchi has lower blood donation rates than other Sri Lanka areas. Aim: To describe the level of donor satisfaction, identify the barriers and factors to increase in-house blood donors of a District General Hospital, Kilinochchi. Materials and Methods: This questionnaire-based crosssectional study was conducted with 193 in-house voluntary blood donors in blood bank at District General Hospital (DGH) Kilinochchi, Sri Lanka, from April 2021 to July 2021. Donors with at least one year of residence in Northern province were included, and data was collected by pretested, self-administered questionnaire. The parameters assessed were socio-demographic data, donor satisfaction, blood donation information receiving methods, factors affecting blood donation, and factors to improve blood donation. Data entered and analysed using Statistical Package of Social Sciences (SPSS) version 22.0. Results: Out of total 193, 120 (62.17%) were regular donors. Majority donors were male donors (168,87.04%) and among 25-35 years group (85,44.05%). Most had completed grade 13 exam (66,34.19%), only 28 (14.5%) had a degree and 106 (54.92%) donors were within 10 km from the blood bank. The majority of 122 (63.21%) were aware from a friend, and 16.06% (n=31) were aware from Youtube. Gaining information from a friend was statistically associated with the distance to the blood bank (p-value=0.036). A majority 190 (98.44%) of the in-house blood donors were satisfied with the services provided by the blood bank staff, and 188, 97.40% had stated they are confident about the facilities provided by the blood bank for in-house blood donation. Conclusion: Majority of the in-house blood donors were satisfied with the services provided by the blood bank staff and blood bank facilities. Delivering information regarding blood donation via friends to the population who reside close to the blood bank and awareness programs by health care workers would be the best method to improve in-house blood donations in this area.

Mobile Vehicle Measurement of Urban Atmospheric CH4/C2H6 Using a Midinfrared Dual-Gas Sensor System Based on Interband Cascade Laser Absorption Spectroscopy

A vehicle-mounted mobile mid-infrared dual-gas sensor system for simultaneous detection of methane (CH₄) and ethane (C₂H₆) was developed using two interband cascade lasers (ICLs) for targeting the absorption line of CH₄ at 3038.5 cm^-1 and the line of C₂H₆ at 2996.88 cm^-1, respectively. Two multi-pass gas cells (MPGCs) with an absorption path length of 26.4 m were utilized. The detection limit (1&#x03C3;) of CH₄ using wavelength modulation spectroscopy (WMS) is &#x007E; 4.1 parts-per-billion by volume (ppbv) with a 0.5 s averaging time, and the detection limit (1&#x03C3;) of C₂H₆ is &#x007E; 2.9 ppbv with a 0.5 s averaging time. Vehicle-mounted mobile measurement ability of the dual-gas sensor was enabled by a remote monitoring system based on an embedded Linux processor and a cloud server. Atmospheric CH₄ and C₂H₆ monitoring was performed for 48 hours on the Jilin University campus to evaluate the performance of the sensor system. Vehicle-mounted mobile campaigns were carried out both on the Jilin University campus and in the Changchun Area for dual-gas concentration mapping. The sensor system shows the merits of remote monitoring and mobile gas sensing ability in field applications.

Mobile On-Road Measurements of Aerosol Optical Properties during MOABAI Campaign in the North China Plain

We present the mapping at fine spatial scale of aerosol optical properties using a mobile laboratory equipped with LIDAR (Light Detection And Ranging), sun photometer and in situ instruments for performing on-road measurements. The mobile campaign was conducted from 9 May to 19 May 2017 and had the main objective of mapping the distribution of pollutants in the Beijing and North China Plain (NCP) region. The highest AOD (Aerosol Optical Depth) at 440 nm of 1.34 and 1.9 were recorded during two heavy pollution episodes on 18 May and 19 May 2017, respectively. The lowest Planetary Boundary Layer (PBL) heights (0.5–1.5 km) were recorded during the heavy pollution events, correlating with the highest AOD and southern winds. The transport of desert dust from the Gobi Desert was captured during the mobile measurements, impacting Beijing during 9–13 May 2017. Exploring the NCP outside Beijing provided datasets for regions with scarce ground measurements and allowed the mapping of high aerosol concentrations when passing polluted cities in the NCP (Baoding, Tianjin and Tangshan) and along the Binhai New Area. For the first time, we provide mass concentration profiles from the synergy of LIDAR, sun photometer and in situ measurements. The case study along the Binhai New Area revealed mean extinction coefficients of 0.14 ± 0.10 km−1 at 532 nm and a mass concentration of 80 ± 62 μg/m3 in the PBL (&lt;2 km). The highest extinction (0.56 km−1) and mass concentrations (404 μg/m3) were found in the industrial Binhai New Area. The PM10 and PM2.5 fractions of the total mass concentration profiles were separated using the columnar size distribution, derived from the sun photometer measurements. This study offers unique mobile datasets of the aerosol optical properties in the NCP for future applications, such as satellite validation and air quality studies.

19. Delivery of Flu Immunizations to Healthcare Workers During COVID-19 Pandemic Using a Multidisciplinary Flu Team Approach in a Tertiary Center in the Middle East

BackgroundCompliance with influenza immunization in HCW remains a global challenge, uptake in the Middle East has been reported at 24.7% due to limited access and awareness (1). We aim to report a successful campaign by establishing a multidisciplinary Flu Team during ongoing COVID-19 pandemic.MethodsA multidisciplinary Flu team taskforce was assembled representing all stakeholders to include: Occupational Health, Nursing, Operations, Infomatics, Pharmacists and Administrative staff in July 2020. A pivot was made to switch location from previous year visits to an established vaccine center (ballroom) to a mobile campaign. From July to November 1st, the team met on a regular basis with 90 stakeholders to launch and monitor the ongoing immunizations. Electronic medical record (EPIC) tools such as One Click and Express Lane facilitated nursing check-in, documentation and immunization at one stop and eliminated previously used registration by other staff. EPIC Clarity feature facilitated reporting of compliance for managers and leadership. Ongoing education and awareness of immunization were ongoing through various platforms of communication such as huddles, phone screens, elevators, lounges, virtual grand rounds and corporate intranet communication and website videos.ResultsOf the 3578 healthcare workers, 3,399 were immunized (95%) from September 2020 until the end of October 2020. There were 86 (2.4%) employees exempted during this period due to medical reasons or excused leaves ( e.g military, maternity), Figure 1. Compliance differed among functions, 95.86% physicians, 97.2% clinical and nursing, 92% academics, 94.96% finance, 91.15% human resources, 92.1% infomatics, 60% legal, 80.6% operations. Only 93 (2.6%) were non-compliant.Employee Flu Immunization Hospital Overall Compliance in 2020 Compliance by Function in 2020 ConclusionInfluenza illness adds an additional burden to the healthcare workforce during COVID-19. A multidisciplinary and collaborative team of teams approach delivered higher compliance for flu immunization than reported in the Middle East and enhanced by the use of state of the art technology. Convenience, educational awareness, free and safe access supported further the compliance with vaccination. To our knowledge, our 2020 flu campaign is the first successful experience reported in the Middle East during the current pandemic.Disclosures All Authors: No reported disclosures

A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality

Air pollution in urban areas is driven by emission sources and modulated by local meteorology, including the effects of urban form on wind speed and ventilation, and thus varies markedly in space and time. Recently, mobile measurement campaigns have been conducted in urban areas to measure the spatial distribution of air pollutant concentrations. While the main focus of these studies has been revealing spatial patterns in mean (or median) concentrations, they have mostly ignored the temporal aspects of air pollution. However, assessing the temporal variability of air pollution is essential in understanding the integrated exposure of individuals to pollutants above critical thresholds. Here, we examine the role of urban land use in mediating the effect of regional meteorology on Nitrogen Dioxide (NO 2 ) concentrations measured in different regions of Oakland, CA. Inspired by Land Use Regression (LUR) models, we cluster 30-m road segments in the urban area based on their land use. The concentration data from the resulting clusters are stratified based on seasonality and conditionally averaged based on concurrent wind speeds. The clustering analysis yielded 7 clusters, with 4 of them chosen for further statistical analysis due to their large sample sizes. Two of the four clusters demonstrated in winter a strong negative linear relationship between NO 2 concentration and wind speed ( R 2 > 0.87) with a slope of approximately 3 ppb/m s -1 . A weaker correlation and flatter slope was found for the cluster representing road segments belonging to interstate highways ( R 2 > 0.73 and slope < 2 ppb/m s -1 ). No significant relationship was found during the summer season. These findings are consistent with the concept of strong vertical mixing due to highway traffic and increased surface heat fluxes during summer weakening the relationship between wind speed and NO 2 concentrations. In summary, the clustering analysis framework presented here provides a novel tool for use with large-scale mobile measurements to reveal the effect of urban land form on the temporal dynamics of pollutant concentrations and ultimately human exposure. • Clustering framework developed for spatio-temporal analysis of mobile measurements. • Domain-related procedure for selecting number of clusters in k-means is presented. • Effect of meteorology on pollutant levels as mediated by land-use is investigated. • Wind speed is only effective in reducing pollutant levels in some urban regions.