Mobile Campaigns Research Articles

Increased COVID-19 Testing Rates Following Combined Door-to-Door and Mobile Testing Facility Campaigns in Oslo, Norway, a Difference-in-Difference Analysis.

High testing rates limit COVID-19 transmission. Attempting to increase testing rates, Stovner District in Oslo, Norway, combined door-to-door campaigns with easy access testing facilities. We studied the intervention’s impact on COVID-19 testing rates. The Stovner District administration executed three door-to-door campaigns promoting COVID-19 testing accompanied by drop-in mobile COVID-19 testing facilities in different areas at 2-week intervals. We calculated testing rates pre- and post-campaigns using data from the Norwegian emergency preparedness register for COVID-19 (Beredt C19). We applied a difference-in-difference approach using ordinary least square regression models and robust standard errors to estimate changes in COVID-19 testing rates. Door-to-door visits reached around one of three households. Intervention and comparison areas had identical testing rates before the intervention, and we observed an increase in intervention areas after the campaigns. We estimate a 43% increase in testing rates over the first three days following the door-to-door campaigns (p = 0.28), corresponding to an additional 79 (95% confidence interval, −54 to 175) people tested. Considering the shape of the time series curves and the large effect estimate, we find it highly likely that the campaigns had a substantial positive impact on COVID-19 testing rates, despite a p-value above the conventional levels for statistical significance. The results and the feasibility of the intervention suggest that it may be worth implementing in similar settings.

The Rapid Assessment of Aggregated Wastewater Samples for Genomic Surveillance of SARS-CoV-2 on a City-Wide Scale.

Throughout the course of the ongoing SARS-CoV-2 pandemic there has been a need for approaches that enable rapid monitoring of public health using an unbiased and minimally invasive means. A major way this has been accomplished is through the regular assessment of wastewater samples by qRT-PCR to detect the prevalence of viral nucleic acid with respect to time and location. Further expansion of SARS-CoV-2 wastewater monitoring efforts to include the detection of variants of interest/concern through next-generation sequencing has enhanced the understanding of the SARS-CoV-2 outbreak. In this report, we detail the results of a collaborative effort between public health and metropolitan wastewater management authorities and the University of Louisville to monitor the SARS-CoV-2 pandemic through the monitoring of aggregate wastewater samples over a period of 28 weeks. Through the use of next-generation sequencing approaches the polymorphism signatures of Variants of Concern/Interest were evaluated to determine the likelihood of their prevalence within the community on the basis of their relative dominance within sequence datasets. Our data indicate that wastewater monitoring of water quality treatment centers and smaller neighborhood-scale catchment areas is a viable means by which the prevalence and genetic variation of SARS-CoV-2 within a metropolitan community of approximately one million individuals may be monitored, as our efforts detected the introduction and emergence of variants of concern in the city of Louisville. Importantly, these efforts confirm that regional emergence and spread of variants of interest/concern may be detected as readily in aggregate wastewater samples as compared to the individual wastewater sheds. Furthermore, the information gained from these efforts enabled targeted public health efforts including increased outreach to at-risk communities and the deployment of mobile or community-focused vaccination campaigns.

Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images.

Outdoor ultrafine particles (UFP, <0.1 μm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R2 = 0.54) outperformed the CNN (R2 = 0.47) and land use regression (LUR) models (R2 = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.

Methane (CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;) sources in Krakow, Poland: insights from isotope analysis

Abstract. Methane (CH4) emissions from human activities are a threat to the resilience of our current climate system. The stable isotopic composition of methane (δ13C and δ2H) allows us to distinguish between the different CH4 origins. A significant part of the European CH4 emissions, 3.6 % in 2018, comes from coal extraction in Poland, the Upper Silesian Coal Basin (USCB) being the main hotspot. Measurements of CH4 mole fraction (χ(CH4)), δ13C, and δ2H in CH4 in ambient air were performed continuously during 6 months in 2018 and 2019 at Krakow, Poland, in the east of the USCB. In addition, air samples were collected during parallel mobile campaigns, from multiple CH4 sources in the footprint area of the continuous measurements. The resulting isotopic signatures from sampled plumes allowed us to distinguish between natural gas leaks, coal mine fugitive emissions, landfill and sewage, and ruminants. The use of δ2H in CH4 is crucial to distinguish the fossil fuel emissions in the case of Krakow because their relatively depleted δ13C values overlap with the ones of microbial sources. The observed χ(CH4) time series showed regular daily night-time accumulations, sometimes combined with irregular pollution events during the day. The isotopic signatures of each peak were obtained using the Keeling plot method and generally fall in the range of thermogenic CH4 formation – with δ13C between −59.3 ‰ and −37.4 ‰ Vienna Pee Dee Belemnite (V-PDB) and δ2H between −291 ‰ and −137 ‰ Vienna Standard Mean Ocean Water (V-SMOW). They compare well with the signatures measured for gas leaks in Krakow and USCB mines. The CHIMERE transport model was used to compute the CH4 and isotopic composition time series in Krakow, based on two emission inventories. The magnitude of the pollution events is generally underestimated in the model, which suggests that emission rates in the inventories are too low. The simulated isotopic source signatures, obtained with Keeling plots on each simulated peak, indicate that a higher contribution from fuel combustion sources in the EDGAR v5.0 inventory would lead to a better agreement than when using CAMS-REG-GHG v4.2 (Copernicus Atmosphere Monitoring Service REGional inventory for Air Pollutants and GreenHouse Gases). The isotopic mismatches between model and observations are mainly caused by uncertainties in the assigned isotopic signatures for each source category and the way they are classified in the inventory. These uncertainties are larger for emissions close to the study site, which are more heterogenous than the ones advected from the USCB coal mines. Our isotope approach proves to be very sensitive in this region, thus helping to evaluate emission estimates.

Design and evaluation of mobile monitoring campaigns for exposure assessment in epidemiologic cohorts

BACKGROUND AND AIM: Mobile monitoring has recently made it possible to measure the long-term trends of less commonly measured pollutants. While many different monitoring approaches have been taken, few studies have looked at the importance of study design when the goal is application to epidemiologic cohort studies. We carried out a simulation study to better understand the role of short-term mobile monitoring design on the prediction of long-term air pollution exposure surfaces. Since air pollution concentrations include random and systematic variability, we hypothesized that mobile campaigns will benefit from balanced designs that randomly sample from all seasons of the year, days of the week and hours of the day. METHODS: We simulated various short-term sampling designs using oxides of nitrogen (NOx) monitoring data from California air quality system (AQS) sites. Designs studied included a year-around, Balanced Design and two more common designs from the literature: a Rush Hours and a Business Hours Design. We evaluated the resulting annual average exposure predictions against the observations from each design and against the measured true concentrations. RESULTS:We found that the Balanced Design consistently produced accurate annual averages, while the Rush Hours and Business Hours Designs generally resulted in more biased estimates and model predictions. The superior performance of the Balanced Design was evident when predictions were evaluated against true concentrations; importantly, this superior performance was less detectable when predictions were evaluated against the measurements from the same sampling campaign since these measurements were themselves biased. CONCLUSIONS:Balanced design campaigns are expected to produce generally unbiased, long-term averages. Differential exposure misclassification could result from unbalanced designs, which may result in misleading health effect estimates in epidemiologic investigations. Appropriate study design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. KEYWORDS: air pollution, study design, exposure assessment, oxides of nitrogen, environmental epidemiology, long-term exposure

Using a mobile monitoring campaign to characterize average exposures to ultrafine particulate matter and black carbon for a Seattle-based cohort

BACKGROUND AND AIM: A growing body of evidence has begun linking traffic-related air pollution (TRAP) to brain health, including dementia. Ultrafine particles (UFP, 100 nm diameter) and black carbon (BC) are important components of traffic pollution, both of which may play an important role in the adverse health effects associated with particulate matter. Still, epidemiologic investigations of UFP and BC are limited in part by the absence of relevant long-term exposure models. METHODS: We leveraged an innovative mobile monitoring campaign specifically designed to assess, at high spatial precision, long-term UFP and BC exposure for the Adult Changes in Thought (ACT) study, a Seattle-based, prospective cohort study of aging and the brain. We systematically collected repeated short-term monitoring samples (~275 drive days) and used these to build annual-average UFP and BC universal kriging models with partial least squares regression summarizing hundreds of geographic covariate predictors. RESULTS:The external validation results indicated low model bias and high precision (RMSE: 930 UFP particles/cm³, 58 BC ng/m³; R2: 0.87 for UFP, 0.85 for BC). Predicted annual-average UFP and BC exposure for the ACT cohort had a median (IQR) of 6,800 (6,000-7,800) pt/cm³ and 530 (460-590) ng/m³, respectively; those values are generally lower than levels reported in other cities around the world. Similar to past studies, predicted concentrations were highest near the airport, downtown and industrial areas as well as along major highways. Furthermore, airport locations had much higher UFP levels relative to BC. CONCLUSIONS:CONCLUSIONS: We successfully predicted annual-average concentrations of UFP and BC throughout a large metropolitan area using a novel and extensive mobile monitoring campaign. This use of mobile monitoring data as inputs to prediction models creates opportunities for epidemiologic investigations of traffic-related air pollution exposures, including UFP and BC. KEYWORDS: mobile monitoring, ultrafine particulate matter (UFP), exposure assessment, study design

Predicting Within-City Variations in Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Columbia Using Open Source Data and Images

BACKGROUND AND AIM: Outdoor ultrafine particles (UFP; 100 nm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health such as cardiovascular mortality and brain tumour incidence. Traditional regression methods for estimating spatial distributions of ambient air pollution depend on extensive, curated geospatial information system databases. This has resulted in a disparity wherein little is known about local air pollution in the data-sparse settings of low- and middle- income countries. We used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. METHODS: We conducted a large-scale mobile monitoring campaign over twenty days in 2019. Land use regression (LUR) models were developed using land use parameters from curated and open-source databases. Convolutional Neural Network (CNN) models were developed using Google Maps satellite view and street view images. Combined models were developed by combining predictions from the LUR and CNN models. After training in the training and validation sets, predictions were generated in the test set and compared to measured values. RESULTS:The combined UFP model (R2=0.54) outperformed the CNN (R2=0.47) and land use regression (LUR) models (R2=0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45 respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. CONCLUSIONS:Estimates from these models can then be applied to population-based cohorts in order to evaluate population health risks. Additionally, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modelling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations. KEYWORDS: Ultrafine particles, Black Carbon, Deep learning, Images, Land use regression

Mobile monitoring and spatial predictionof black carbonin Cairo, Egypt.

This study harnessesthe power of mobile data in developing a spatialmodel for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300km were travelled across a widespread 525km of routes. Reported average BC values were around 20µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared-in terms of mean square error (MSE) and mean absolute error (MAE)-with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).

To achieve 95-95-95 targets we must reach men and youth: High level of knowledge of HIV status, ART coverage, and viral suppression in the Botswana Combination Prevention Project through universal test and treat approach.

BackgroundIncreasing HIV treatment coverage is crucial to reducing population-level HIV incidence.MethodsThe Botswana Combination Prevention Project (BCPP) was a community randomized trial examining the impact of multiple prevention interventions on population-level HIV incidence and was conducted from October 2013 through June 2017. Home and mobile campaigns offered HIV testing to all individuals ≥ age 16. All identified HIV-positive persons who were not on antiretroviral therapy (ART) were referred to treatment and tracked to determine linkage to care, ART status, retention in treatment, and viral suppressionResultsOf an estimated total of 14,270 people living with HIV (PLHIV) residing in the 15 intervention communities, BCPP identified 13,328 HIV-positive persons (93%). At study start, 10,703 (80%) of estimated PLHIV knew their status; 2,625 (20%) learned their status during BCPP, a 25% increase with the greatest increases occurring among men (37%) and youth (77%). At study start, 9,258 (65%) of estimated PLHIV were on ART. An additional 3,001 persons started ART through the study. By study end, 12,259 had initiated and were retained on ART, increasing coverage to 93%. A greater increase in ART coverage was achieved among men (40%) compared to women (29%). Of the 11,954 persons who had viral load (VL) test results, 11,687 (98%) were virally suppressed (HIV-1 RNA ≤400 copies/mL). Overall, 82% had documented VL suppression by study end.ConclusionsKnowledge of HIV-positive status and ART coverage increased towards 95–95 targets with universal testing, linkage interventions, and ART. The increases in HIV testing and ART use among men and youth were essential to reaching these targets.Clinical trial numberNCT01965470.

The use of thermal ablation in diverse cervical cancer "screen-and-treat" service platforms in Zambia.

Thermal ablation (TA) was implemented in public sector cervical cancer prevention services in Zambia in 2012. Initially introduced as a treatment modality in primary healthcare clinics, it was later included in mobile outreach campaigns and clinical research trials. We report the feasibility, acceptability, safety, and provider uptake of TA in diverse clinical contexts. Screening services based on visual inspection with acetic acid were offered by trained nurses to non-pregnant women aged 25-59years. Women with a type 1 transformation zone (TZ) were treated with same-visit TA. Those with a type 2 or 3TZ, or suspicious for cancer, were managed with same-visit electrosurgical excision or punch biopsy, respectively. A provider survey was conducted. Between 2012 and 2020, 2123 women were treated with TA: primary healthcare clinics, n=746; mobile outreach clinics, n=1127; research clinics, n=250. Of the 996 women treated in primary healthcare and research clinics, 359 (48%) were HIV positive. Mild cramping during treatment was the most common adverse effect. No treatment interruptions occurred. No major complications were reported in the early (6weeks) follow-up period. Providers expressed an overwhelming preference for TA over cryotherapy. TA was feasible, safe, and acceptable in diverse clinical contexts. It was the preferred ablation method of providers when compared with cryotherapy.

A Decision Analysis Model for the Brand Experience of Branded Apps Using Consistency Fuzzy Linguistic Preference Relations

Branded apps are not only an important platform for enterprises and customers to have real-time interactions and communicate marketing messages, but also a new business model that encourages value co-creation between the two. In order to explore the impact of branded apps on customers, this study constructs a fuzzy multi-criteria decision making (FMCDM) analysis model, and it uses consistent fuzzy linguistic preference relations (CFLPR) to set up a symmetric pairwise comparison matrix, which greatly reduces the complexity and error rate of calculations. Empirical research findings show that brand experience attributes and the influence of brand experience on customer loyalty and satisfaction can be more accurately measured. As a consequence of this study, we show that, among the brand experience facets of two retail chain branded apps, behavioral experience is the most favored, while affective experience is the least favored. Furthermore, brand attachment and active participation should be strengthened to enhance customer loyalty. Through the analytical model employed in this study, enterprises can regularly monitor changes in the brand experience preferences of branded app users and evaluate app performance to flexibly adjust mobile device-based marketing campaigns and strategies. It can also aid enterprises in using mobile devices effectively to improve customer loyalty and address the issue of diminishing brand loyalty.

Strong tie or weak tie? Exploring the impact of group-formation gamification mechanisms on user emotional anxiety in social commerce

ABSTRACT Customers can raise various aspects of concerns and anxieties during their participation in gamified social mobile marketing campaigns. To gain insights into how to address these anxieties, this research explores the impact of group-formation gamification mechanisms on users’ emotional anxiety and their intentions to participate in such campaigns. A research model depicting group-formation gamification strategies, user anxieties and participation intentions in gamified social mobile marketing campaigns was proposed and examined through experiments involving 232 participants, which were triangulated by electroencephalogram (EEG) tests and follow-up interviews. The results of this study showed that compared with weak-tie group-formation mechanisms, strong-tie mechanisms can result in a lower level of user emotional anxiety, including reductions in user manipulation anxiety, user privacy anxiety and social image anxiety, which in turn led to higher user intentions to participate in the gamified social mobile marketing campaign. Moreover, it was found that user gender and disposable incomes had significant moderating effects on user anxiety during their interactions with the campaign.

How push messaging impacts consumer spending and reward redemption in store-loyalty programs

Retailer loyalty programs (LPs) are pervasive in grocery retailing. However, participant spending and redemption typically wear off over time and traditional communication has not revealed very effective at maintaining program engagement. We study the impact of in-app mobile push notifications on consumer participation and reward collection in store-loyalty programs. Using a unique data set covering consumer spending before and during such a program, we estimate the effect of push messaging on expenditure and reward redemption during the program. We report positive effects of push messages on spending, and even stronger effects on redemption, relative to a control group not receiving such messages. Due to the savings dynamics, the total spending impact is larger for messages sent early on rather than late in the program, while the opposite holds for the total number of stamps redeemed. Conditioning on observable consumer characteristics, we allow for heterogeneous treatment effects and find that the spending and redemption effects of push messaging increase with high levels of pre-program spending. Our findings reveal which loyalty-program stakeholders benefit the most from mobile marketing campaigns, and help to formulate rules for campaign scheduling and targeting.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions.

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

Initiation of hepatitis C treatment in two rural Rwandan districts: a mobile clinic approach

BackgroundTo eliminate hepatitis C, Rwanda is conducting national mass screenings and providing to people with chronic hepatitis C free access to Direct Acting Antivirals (DAAs). Until 2020, prescribers trained and authorized to initiate DAA treatment were based at district hospitals, and access to DAAs remains expensive and geographically difficult for rural patients. We implemented a mobile clinic to provide DAA treatment initiation at primary-level health facilities among people with chronic hepatitis C identified through mass screening campaigns in rural Kirehe and Kayonza districts.MethodsThe mobile clinic team was composed of one clinician authorized to manage hepatitis, one lab technician, and one driver. Eligible patients received same-day clinical consultations, counselling, laboratory tests and DAA initiation. Using clinical databases, registers, and program records, we compared the number of patients who initiated DAA treatment before and during the mobile clinic campaign. We assessed linkage to care during the mobile clinical campaign and assessed predictors of linkage to care. We also estimated the cost per patient of providing mobile services and the reduction in out-of-pocket costs associated with accessing DAA treatment through the mobile clinic rather than the standard of care.ResultsPrior to the mobile clinic, only 408 patients in Kirehe and Kayonza had been initiated on DAAs over a 25-month period. Between November 2019 and January 2020, out of 661 eligible patients with hepatitis C, 429 (64.9%) were linked to care through the mobile clinic. Having a telephone number and complete address recorded at screening were strongly associated with linkage to care. The cost per patient of the mobile clinic program was 29.36 USD, excluding government-provided DAAs. Providing patients with same-day laboratory tests and clinical consultation at primary-level health facilities reduced out-of-pocket expenses by 9.88 USD.ConclusionThe mobile clinic was a feasible strategy for providing rapid treatment initiation among people chronically infected by hepatitis C, identified through a mass screening campaign. Compared to the standard of care, mobile clinics reached more patients in a much shorter time. This low-cost strategy also reduced out-of-pocket expenditures among patients. However, long-term, sustainable care would require decentralization to the primary health-centre level.

Mobile Monitoring for the Spatial and Temporal Assessment of Local Air Quality (NO2) in the City of London

This paper reports on the analysis and findings of the data collected during a mobile air quality campaign commissioned by the City of London Corporation (CoL). This was done using an equipped vehicle capable of taking continuous precision measurements of local air quality while travelling within the City. Several comparative analyses on measured Nitrogen Dioxide (NO2) data have been performed between Smogmobile data and those available from CoL precision systems as well as with indicative systems, namely Diffusion Tubes, distributed across the City. Key findings highlight that data collected from the Smogmobile, in terms of average concentration of NO2 across the City (62 µg/m3), are very similar to those obtained by averaging the values from the 48 indicative systems (59.5 µg/m3), with an error of just 4%. Overall, this study demonstrates significant potential and value in using mobile air quality measurements to support assessment of air quality over large areas by Local authorities.

Test Charts for Evaluating Imaging and Point Cloud Quality of Mobile Mapping Systems for Urban Street Space Acquisition

Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.

Validating and Comparing Highly Resolved Commercial "Off the Shelf" PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment.

Particulate matter is a common health hazard, and under certain conditions, an ecological threat. While many studies were conducted in regard to air pollution and potential effects, this paper serves as a pilot scale investigation into the spatial and temporal variability of particulate matter (PM) pollution in arid urban environments in general, and Beer-Sheva, Israel as a case study. We explore the use of commercially off the shelf (COTS) sensors, which provide an economical solution for spatio-temporal measurements. We started with a comparison process against an A-grade meteorological station, where it was shown that under specific climatic conditions, a number of COTS sensors were able to produce robust agreement (mean ). The second stage examined the COTS sensors that were proven accurate in a mobile measurement campaign. Finally, data collected was compared to a validated satellite prediction model. We present how these tests and COTS sensor-kits could then be used to further explain the continuity and dispersion of particulate matter in similar areas.

Pneumococcal Immunisation Coverage Among Infants Between 4-12 Months in Nyamira County, Kenya

Background : Pneumonia is the leading killer among all infectious diseases worldwide. Though treatable, it often results in high mortality and morbidity therefore putting pressure on available health resources. The best intervention for pneumonia is prevention through vaccination. Objective . To determine pneumococcal vaccine coverage among infants between 4-12 months in Nyamira County Methods : A cross sectional design was used in this study. Using questionnaires. Caregivers were interviewed and a checklist was used to review the vaccination records. Data was analyzed using descriptive statistics and inferential statistics. Results : The results indicated that coverage of 82.5% for PCV 10 vaccine is below the recommended 90%. Caregivers were predominantly female, 97% and in informal employment. Among the demographic factors of the cohort, level of education was found to be statistically significant with p values of 0.029 as calculated using Fischer’s Exact. Care Givers were asked whether they had other children that were below 5 years so as to determine whether their previous experience with their children made any impact on the vaccine coverage. This demographic factor was also found to be significant with a P value of 0.019 as calculated using Chi Square. and the number of other children Caregivers had (besides the infant being studied) that were below 5 years were found to and respectively. Conclusion and Recommendations: Coverage of pneumococcal vaccine in Borabu Sub-County, Nyamira County was (82.5%), still below the internationally recommended target of 90%. Uptake of PCV 10 vaccine was found to be influenced by Knowledge about the PCV 10 vaccine, level of education of Caregivers and the age of children that Caregivers had who were below 5 years. The coverage of PCV 10 vaccine in Borabu Sub County in Nyamira County can be enhanced by increasing knowledge of Caregivers on PCV 10 vaccine and by increasing compliance to the vaccination schedule. This can be done through various channels such as use of mobile phone-based reminders and community sensitization campaigns. A study can be done to identify effective interventions for delivering health education about pneumococcal vaccine in rural populations. In addition, interventional studies may be done to test effectiveness of reminder systems on immunization uptake. Keywords: uptake, coverage, PCV 10, pneumococcal vaccine. DOI: 10.7176/JNSR/11-22-01 Publication date: November 30 th 2020

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance.

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM2.5), inhalable particulate matter (PM10), and nitrogen dioxide (NO2) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R2) for the LUR models ranged from 0.53-0.87 (PM2.5), 0.53-0.85 (PM10), and 0.33-0.67 (NO2). The performance of the daytime/nighttime LUR models for PM2.5 and PM10 was better than that of the full-day models according to the results of model adjusted R2 and validation R2. Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO2 to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM2.5/PM10 has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM2.5 and PM10. Nighttime could be a critical exposure period, due to high pollutant concentrations.