Monitoring of Ultrafine Particles in Rural and Urban Environments

2013 ◽  
pp. 271-282
Author(s):  
F. Lenartz ◽  
C. Mentink ◽  
M. Severijnen ◽  
B. Bergmans
Keyword(s):  
Ultrafine Particles ◽  
Urban Environments ◽  
Rural And Urban

scholarly journals Gut Microbiota Dynamics in Natural Populations of Pintomyia evansi under Experimental Infection with Leishmania infantum

2021 ◽  
Vol 9 (6) ◽  
pp. 1214
Author(s):  
Rafael José Vivero ◽  
Victor Alfonso Castañeda-Monsalve ◽  
Luis Roberto Romero ◽  
Gregory D. Hurst ◽  
Gloria Cadavid-Restrepo ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Leishmania Infantum ◽  
Natural Populations ◽  
Illumina Miseq ◽  
Urban Environments ◽  
High Load ◽  
Leishmania Infection ◽  
Experimental Conditions ◽  
Rural And Urban ◽  
Negative Effect

Pintomyia evansi is recognized by its vectorial competence in the transmission of parasites that cause fatal visceral leishmaniasis in rural and urban environments of the Caribbean coast of Colombia. The effect on and the variation of the gut microbiota in female P. evansi infected with Leishmania infantum were evaluated under experimental conditions using 16S rRNA Illumina MiSeq sequencing. In the coinfection assay with L. infantum, 96.8% of the midgut microbial population was composed mainly of Proteobacteria (71.0%), followed by Cyanobacteria (20.4%), Actinobacteria (2.7%), and Firmicutes (2.7%). In insect controls (uninfected with L. infantum) that were treated or not with antibiotics, Ralstonia was reported to have high relative abundance (55.1–64.8%), in contrast to guts with a high load of infection from L. infantum (23.4–35.9%). ASVs that moderately increased in guts infected with Leishmania were Bacillus and Aeromonas. Kruskal–Wallis nonparametric variance statistical inference showed statistically significant intergroup differences in the guts of P. evansi infected and uninfected with L. infantum (p < 0.05), suggesting that some individuals of the microbiota could induce or restrict Leishmania infection. This assay also showed a negative effect of the antibiotic treatment and L. infantum infection on the gut microbiota diversity. Endosymbionts, such as Microsporidia infections (<2%), were more often associated with guts without Leishmania infection, whereas Arsenophonus was only found in guts with a high load of Leishmania infection and treated with antibiotics. Finally, this is the first report that showed the potential role of intestinal microbiota in natural populations of P. evansi in susceptibility to L. infantum infection.

Damage assessment mapping of rural environments; integration of SAR and Optical data

2021 ◽  
Author(s):  
Shiran Havivi ◽  
Stanley R. Rotman ◽  
Dan G. Blumberg ◽  
Shimrit Maman
Keyword(s):  
Natural Disaster ◽  
Rural Areas ◽  
Urban Areas ◽  
Damage Assessment ◽  
Vegetation Index ◽  
Urban Environments ◽  
Optical Data ◽  
Damage Level ◽  
Rural Environments ◽  
Rural And Urban

&lt;p&gt;The damage caused by a natural disaster in rural areas differs in nature, extent, landscape and in structure, from the damage in urban environments. Previous and current studies focus mainly on mapping damaged structures in urban areas after catastrophe events such as an earthquake or tsunami. Yet, research focusing on the damage level or its distribution in rural areas is absent. In order to apply an emergency response and for effective disaster management, it is necessary to understand and characterize the nature of the damage in each different environment.&amp;#160;&lt;/p&gt;&lt;p&gt;Havivi et al. (2018), published a damage assessment algorithm that makes use of SAR images combined with optical data, for rapid mapping and compiling a damage assessment map following a natural disaster. The affected areas are analyzed using interferometric SAR (InSAR) coherence. To overcome the loss of coherence caused by changes in vegetation, optical images are used to produce a mask by computing the Normalized Difference Vegetation Index (NDVI) and removing the vegetated area from the scene. Due to the differences in geomorphological settings and landuse\landcover between rural and urban settlements, the above algorithm is modified and adjusted by inserting the Modified Normalized Difference Water Index (MNDWI) to better suit rural environments and their unique response after a disaster. MNDWI is used for detection, identification and extraction of waterbodies (such as irrigation canals, streams, rivers, lakes, etc.), allowing their removal which causes lack of coherence at the post stage of the event. Furthermore, it is used as an indicator for highlighting prone regions that might be severely affected pre disaster event. Thresholds are determined for the co-event coherence map (&amp;#8804; 0.5), the NDVI (&amp;#8805; 0.4) and the MNDWI (&amp;#8805; 0), and the three layers are combined into one. Based on the combined map, a damage assessment map is generated.&amp;#160;&lt;/p&gt;&lt;p&gt;As a case study, this algorithm was applied to the areas affected by multi-hazard event, following the Sulawesi earthquake and subsequent tsunami in Palu, Indonesia, which occurred on September 28th, 2018. High-resolution COSMO-SkyMed images pre and post the event, alongside a Sentinel-2 image pre- event are used as inputs. The output damage assessment map provides a quantitative assessment and spatial distribution of the damage in both the rural and urban environments. The results highlight the applicability of the algorithm for a variety of disaster events and sensors. In addition, the results enhance the contribution of the water component to the analysis pre and post the event in rural areas. Thus, while in urban regions the spatial extent of the damage will occur in its proximity to the coastline or the fault, rural regions, even in significant distance will experience extensive damage due secondary hazards as liquefaction processes.&amp;#160; &amp;#160; &amp;#160;&lt;/p&gt;

scholarly journals Bounding the Practical Error of Path Loss Models

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Caleb Phillips ◽  
Douglas Sicker ◽  
Dirk Grunwald
Keyword(s):  
A Priori ◽  
Path Loss ◽  
Urban Environments ◽  
Production Networks ◽  
Modeling Error ◽  
Propagation Models ◽  
Performance Accuracy ◽  
Rural And Urban ◽  
Standard Models ◽  
Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios wherea prioripredictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.

scholarly journals Evidence and theory for lower rates of depression in larger US urban areas

2021 ◽  
Vol 118 (31) ◽  
pp. e2022472118
Author(s):  
Andrew J. Stier ◽  
Kathryn E. Schertz ◽  
Nak Won Rim ◽  
Carlos Cardenas-Iniguez ◽  
Benjamin B. Lahey ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Determinants ◽  
Urban Areas ◽  
Urban Environments ◽  
City Size ◽  
Built Environments ◽  
Rural And Urban ◽  
Us Cities ◽  
Systematic Framework

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual’s accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

scholarly journals Loading, transport, and treatment of emerging chemical and biological contaminants of concern in stormwater

2021 ◽  
Author(s):  
Sumaiya Saifur ◽  
Courtney M. Gardner
Keyword(s):  
Water Quality ◽  
Health Knowledge ◽  
Antibiotic Resistance Genes ◽  
Environmental Water ◽  
The United States ◽  
Urban Environments ◽  
Transport Behavior ◽  
Rural And Urban ◽  
Biological Contaminants ◽  
Quality Water

Abstract Stormwater is a largely uncontrolled source of pollution in rural and urban environments across the United States. Concern regarding the growing diversity and abundance of pollutants in stormwater as well as their impacts on water quality has grown significantly over the past several decades. In addition to conventional contaminants like nutrients and heavy metals, stormwater is a well-documented source of many contaminants of emerging concern, which can be toxic to both aquatic and terrestrial organisms and remain a barrier to maintaining high quality water resources. Chemical pollutants like pharmaceuticals and personal care products, industrial pollutants such as per- and polyfluoroalkyl substances, and tire wear particles in stormwater are of great concern due to their toxic, genotoxic, mutagenic and carcinogenic properties. Emerging microbial contaminants such as pathogens and antibiotic resistance genes also represent significant threats to environmental water quality and human health. Knowledge regarding the transport, behavior, and the remediation capacity of these pollutants in runoff is key for addressing these pollutants in situ and minimizing ecosystem perturbations. To this end, this review paper will analyze current understanding of these contaminants in stormwater runoff in terms of their transport, behavior, and bioremediation potential.

scholarly journals Sleep and BMI in South African urban and rural, high and low-income preschool children

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dale E. Rae ◽  
Simone A. Tomaz ◽  
Rachel A. Jones ◽  
Trina Hinkley ◽  
Rhian Twine ◽  
...  
Keyword(s):  
Physical Activity ◽  
Preschool Children ◽  
South African ◽  
Low Income ◽  
Urban Environments ◽  
Z Score ◽  
Cross Sectional ◽  
Rural And Urban ◽  
Sleep Parameters ◽  
Z Scores

Abstract Background The extent to which income setting or rural and urban environments modify the association between sleep and obesity in young children is unclear. The aims of this cross-sectional observational study were to (i) describe and compare sleep in South African preschool children from rural low-income (RL), urban low-income (UL) and urban high-income (UH) settings; and (ii) test for associations between sleep parameters and body mass index (BMI). Methods Participants were preschoolers (5.2 ± 0.7y, 49.5% boys) from RL (n = 111), UL (n = 65) and UH (n = 22) settings. Height and weight were measured. Sleep, sedentary behaviour and physical activity were assessed using accelerometery. Results UL children had higher BMI z-scores (median: 0.39; interquartile range: − 0.27, 0.99) than the UH (− 0.38; − 0.88, 0.11) and RL (− 0.08; − 0.83, 0.53) children (p = 0.001). The UL children had later bedtimes (p < 0.001) and wake-up times (p < 0.001) and shorter 24 h (p < 0.001) and nocturnal (p < 0.001) sleep durations than the RL and UH children. After adjusting for age, sex, setting, SB and PA, for every hour less sleep obtained (24 h and nocturnal), children were 2.28 (95% CI: 1.28–4.35) and 2.22 (95% CI: 1.27–3.85) more likely, respectively, to belong to a higher BMI z-score quartile. Conclusions Shorter sleep is associated with a higher BMI z-score in South African preschoolers, despite high levels of PA, with UL children appearing to be particularly vulnerable.

A Study of Airborne Wear Particles Generated From a Sliding Contact

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Ulf Olofsson ◽  
Lars Olander ◽  
Anders Jansson
Keyword(s):  
Ultrafine Particles ◽  
Fine Particles ◽  
Urban Environments ◽  
Airborne Particles ◽  
Wear Particles ◽  
Coarse Particles ◽  
Sliding Velocity ◽  
Airborne Particle ◽  
Particle Generation ◽  
Pin On Disk

Recently, much attention has been paid to the influence of airborne particles in the atmosphere on human health. Sliding contacts are a significant source of airborne particles in urban environments. In this study airborne particles generated from a sliding steel-on-steel combination are studied using a pin-on-disk tribometer equipped with airborne-particle counting instrumentation. The instrumentation measured particles in size intervals from 0.01μm to 32μm. The result shows three particle size regimes with distinct number peaks: ultrafine particles with a size distribution peak around 0.08μm, fine particles with a peak around 0.35μm, and coarse particles with a peak around 2 or 4μm. Both the particle generation rate and the wear rate increase with increasing sliding velocity and contact pressure.

scholarly journals Rethinking Depression in Cities: Evidence and Theory for Lower Rates in Larger Urban Areas

2020 ◽  
Author(s):  
Andrew J Stier ◽  
Marc G Berman ◽  
Luis M.A. Bettencourt ◽  
Kathryn E Schertz ◽  
Nak Won Rim ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Determinants ◽  
Urban Areas ◽  
Urban Environments ◽  
City Size ◽  
Built Environments ◽  
Rural And Urban ◽  
Us Cities ◽  
Systematic Framework

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and, at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using three independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

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